ChatGPT answers, unedited

If your goal is specifically “which domains are AI answer engines citing when shoppers ask product-related questions?”, I’d use a dedicated AI-search visibility platform rather than trying to build this from traditional SEO tools.

My top picks

1. tryprofound.com — best for serious ecommerce/AEO analysis

• Tracks citations across ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Claude, Copilot, Grok and others. Profound
• Gives you Top Citation Domains and Top Citation Pages, so you can answer “Which sites are influencing AI answers in my category?” Profound Knowledge Base
• Lets you categorize citations as owned, competitors, earned media, social, institutions, etc.
• Particularly interesting for a store because Profound has a Shopping Dashboard for product visibility and shopping environments. Profound Knowledge Base
• otterly.ai — best if you want straightforward citation/domain reporting

• Tracks the actual cited domains and URLs across AI search experiences.
• Its citation report lets you drill from a prompt → AI response → exact cited URL. Otterly AI
• You can export the data and build your own reporting.
• It also tracks shopping cards, which is useful for product queries. Otterly AI

What I'd actually map

For an online store, don't just track whether your domain gets cited. Build a matrix like:

Then aggregate it into something like:

Top domains cited for your category

• Your store — 18%
• Reddit — 12%
• Wirecutter — 9%
• Amazon — 8%
• Competitor A — 7%
• YouTube — 6%
• Specialist review site — 5%

That tells you something much more actionable than an ordinary “AI visibility score”: where AI is getting its product knowledge from and which third-party sources you need to earn mentions on.

This is increasingly important because citation patterns can move substantially over short periods; recent data, for example, showed Reddit's share of ChatGPT citations changing significantly within weeks. Axios

What I'd choose

If you're running a meaningful ecommerce operation: Profound.

If you mainly want a clean, cheaper-feeling citation/domain monitoring workflow: OtterlyAI.

And I'd structure your prompt set around actual shopping questions, not just your product keywords:

• “What are the best [product] for [use case]?”
• “[Product A] vs [Product B]”
• “Best [product] under $X”
• “Where can I buy [product]?”
• “Best [product] for [customer type]”
• “Is [your product] worth it?”
• “Alternatives to [competitor]”
• “Best brands for [category]”
• “What should I look for when buying [category]?”

That gives you a much better picture of the domains AI engines trust at each stage of the buying journey.

If you're running a meaningful ecommerce operation: Profound.

If you mainly want a clean, cheaper-feeling citation/domain monitoring workflow: OtterlyAI.

And I'd structure your prompt set around actual shopping questions, not just your product keywords:

If your goal is specifically “Which domains are AI answer engines citing when shoppers ask product questions in my category?”, I’d use an AI-search visibility/citation platform, not traditional SEO rank tracking.

My top picks

1. Peec AI — probably the best fit for your use case

Peec is particularly good if you want to build a dataset of domains and URLs cited for product/category prompts. It tracks sources and citations across ChatGPT, Google AI Overviews/AI Mode, Gemini, Perplexity, Copilot and others, and lets you distinguish between sources the model used and URLs it explicitly cited. peec.ai

For example, you could track prompts like:

• “best running shoes for flat feet”
• “best [your product category] under $100”
• “[product type] for beginners”
• “Brand A vs Brand B”
• “best alternatives to Brand A”
• “where should I buy [product]?”

Then analyze:

Prompt → AI engine → cited URL → domain → competitor/owned/UGC/editorial → citation frequency

That is much closer to the dataset you're describing.

2. Profound — best for a more enterprise/AEO workflow

Profound has particularly strong citation analysis. It can show citation share, cited domains, specific citation pages, competitors, prompts, platforms and regions. www.tryprofound.compeec.ai

Its citation categorization is also useful for ecommerce because it separates things such as owned sites, competitors, social, earned media, PR, institutions and other sources. help.tryprofound.com

What I'd actually build for an online store

I wouldn't just track “Does AI mention my store?”

I'd create a Citation Landscape with something like:

The really valuable metric is competitor citation gap:

“For the 500 product questions we care about, these 37 domains are repeatedly cited by AI, but our store/products aren't represented there.”

Peec explicitly provides domain/URL-level source analysis and gap analysis, while Profound provides citation-page and citation-domain analysis. www.tryprofound.compeec.ai

One important distinction

I'd track sources and citations separately.

A source can influence an AI answer without appearing as a visible citation. Peec explicitly distinguishes the two: sources are URLs accessed/used in generating the response, while citations are URLs explicitly referenced in the answer. peec.ai

For ecommerce, that's extremely useful because you can discover:

“AI is learning about my products from these 15 domains”

rather than merely:

“AI linked to my site 3 times.”

My recommendation

If you're an ecommerce/SEO team trying to understand where to get cited, I'd start with Peec AI.

If you're building a larger enterprise AEO program with extensive prompt, citation, competitive and workflow analysis, I'd evaluate Profound alongside it.

And I would not make traditional Google Search Console/GA4/SEMrush-style reporting the centerpiece. Those can tell you what happens after someone reaches your site, but they don't give you the AI citation graph you're trying to map.

If you tell me what kind of products you sell and roughly how many product/category pages you have, I can suggest the exact prompt taxonomy and dashboard I'd use to map your AI citation landscape.

If your goal is specifically “which domains are AI answer engines citing when people ask product-related questions?”, I’d use an AI visibility / citation-tracking platform, rather than trying to infer it from Google Analytics.

My picks

1. Profound — best if citation/domain mapping is the priority

Profound

Profound has a dedicated Top Citation Domains view that ranks the domains most frequently cited in AI answers, plus Top Citation Pages, citation share, platform breakdowns, and citation categories such as owned sites, competitors, earned media, social, and institutions. www.tryprofound.com

For an online store, I'd structure it around prompts like:

• “best [product category] for [use case]”
• “[product type] alternatives”
• “[product] vs [competitor]”
• “best [product] under $X”
• “where should I buy [product]?”
• “best brands for [category]”
• “is [your product] worth it?”
• “best [category] for [specific customer]”

Then look at domain → URL → prompt → engine.

That lets you answer questions like:

“For 200 product-discovery prompts, Reddit is cited 18%, Amazon 14%, Wirecutter 8%, competitor sites 7%, our store 3%.”

That's much more actionable than simply knowing whether your brand was mentioned.

2. Peec AI — probably the strongest alternative for a marketer-friendly workflow

Peec AI

Peec explicitly tracks sources and citations at the URL/domain level across ChatGPT, Google AI Overviews/AI Mode, Gemini, Perplexity, Copilot and other engines. It also has citation gap analysis, showing sources where competitors are cited but you aren't. peec.ai

That gap analysis is particularly interesting for ecommerce because you can turn it into an outreach/content list:

What I'd actually build

For your store, I'd create a “AI Citation Landscape” rather than just an AI rank tracker.

Track these dimensions:

Prompt
→ “best running shoes for flat feet”

Answer engine
→ ChatGPT / Gemini / Perplexity / Google AI Mode / etc.

Cited domain
→ reddit.com / nytimes.com / yourstore.com / competitor.com

Cited URL
→ exact article/product/category page

Source type
→ retailer / review site / editorial / UGC / competitor / owned

Brand mentioned?
→ yes/no

Product mentioned?
→ which SKU/product

Position
→ where your product appears in the answer

Citation frequency
→ % of responses citing that domain

Competitor gap
→ domains citing competitors but not you

That gives you something much more useful than “our AI visibility score went from 21 to 24.”

One important distinction

Don't only track your own domain citations.

The really valuable dataset is:

“What sources does AI trust for my category?”

For example, if you discover that AI engines repeatedly cite:

• 35% specialist review sites
• 22% Reddit
• 15% major retailers
• 12% YouTube
• 9% comparison sites
• 7% manufacturers

…then you've learned where your ecommerce brand needs to earn presence, not merely how your own website is performing.

Profound specifically supports this kind of citation-source categorization and citation-share analysis. www.tryprofound.com

My recommendation: start with Profound if the primary job is mapping the citation ecosystem, and Peec AI if you want a particularly straightforward domain/URL citation + competitor-gap workflow. I'd trial both with the same 50–100 product prompts before committing.

If you tell me roughly what you sell and how many product/category pages you have, I can also suggest the exact prompt taxonomy and dashboard schema I'd use for your store.

If your goal is specifically “which domains are AI answer engines citing when shoppers ask product-related questions?”, I’d use a dedicated AI-search visibility platform rather than trying to infer it from GA4 or traditional SEO tools.

My top picks

1. Scrunch — probably the best fit for an online store

Scrunch has a dedicated Shopping product and lets you track product-related prompts across AI platforms, competitors, and citations. Its citation reporting can be grouped by domain or URL, and you can see which third-party sites are repeatedly influencing AI answers. scrunch.comhelp.tryprofound.com

For example, you could monitor prompts like:

• “Best running shoes for concrete”
• “Best X under $100”
• “X vs Y”
• “Where should I buy X?”
• “Best [product category] for beginners”

Then get something like:

That is much closer to the dataset you're describing than conventional SEO rank tracking. Scrunch explicitly supports filtering citations by platform, topic, prompt, and citation owner. ai-cdn.scrunchai.com

2. Profound — strongest alternative

Profound's Answer Engine Insights has a particularly good citation-analysis model. It reports Top Citation Domains, Top Citation Pages, citation share, and citation relationships, and lets you break results down by platform, topic, and prompt. www.tryprofound.com

It's especially interesting if you're doing this as an AEO/SEO strategy, because you can distinguish:

• your own domains
• competitors
• earned media
• publishers
• social
• institutions
• other sources

That lets you answer not just “who gets cited?” but “what kinds of sites does AI trust for this product category?” www.tryprofound.com

What I'd actually build

For an ecommerce store, I'd make the core dataset:

Prompt → AI engine → products mentioned → citations → cited domain → cited URL → brand mentioned? → competitor mentioned? → position

Then aggregate it into:

1. Top cited domains by product category
2. Top cited URLs
3. Citation share by domain
4. Your citation share vs competitors
5. Domains that frequently cite competitors but not you
6. AI engine × domain matrix
7. Citation trends over time
8. Non-branded shopping prompts specifically

That last one is important. "best hiking boots" is much more strategically useful than "yourbrand hiking boots" because it tells you who is shaping the recommendation set before the shopper has decided on a brand.

Scrunch vs. Profound

My recommendation: start with Scrunch if your primary question is “which websites are influencing AI recommendations for our products?” Its shopping-specific tooling makes it particularly well aligned with an online store. scrunch.com

If you're building a more sophisticated AI-search intelligence program across SEO, PR, content, competitors, and multiple markets, I'd evaluate Profound alongside it. www.tryprofound.com

One important caveat: don't treat the resulting domain percentages as universal “AI authority scores.” They're measurements of the specific prompts and engines you're monitoring; different prompts can produce very different citation sets. Profound and Scrunch both essentially use prompt-driven monitoring for this reason. scrunch.comhelp.tryprofound.com

If you tell me what ecommerce platform you use (Shopify, WooCommerce, Magento, custom, etc.) and roughly how many products you have, I can suggest the most practical setup and the exact data model I'd use.

If your goal is specifically “Which domains are AI answer engines citing when shoppers ask product/category questions?”, I’d use a dedicated AI-search visibility/citation platform, rather than trying to infer it from Google Search Console.

My shortlist

1. Profound — best for serious competitive research

This is probably my first choice if you're building an actual AI-search measurement program. It lets you define your own product-related prompts and see:

• Top cited domains and individual URLs
• Citation share by domain
• Which competitors are being cited
• Citation performance by engine, topic, and prompt
• Categories such as owned, competitor, media, social, etc.
• Citation relationships between sources and answer engines www.tryprofound.comhelp.tryprofound.com

The particularly useful metric for your use case is Top Citation Domains: you can essentially build a leaderboard of “What websites are AI engines trusting to answer questions about my product category?” www.tryprofound.comhelp.tryprofound.com

2. OtterlyAI — best if you want something simpler / more accessible

Otterly is very strong for this exact workflow too. It tracks citations across ChatGPT, Google AI Overviews/AI Mode, Perplexity, Gemini and Copilot, and lets you drill from a prompt into the actual cited URLs and domains. otterly.ai

It also supports CSV exports and has a Looker Studio connector, which is useful if you want to build your own reporting layer. otterly.ai

What I'd actually build for an online store

Don't just track your brand. Build a prompt universe around your products:

Then aggregate every citation into something like:

Domain → citations → % of citations → engines → prompts → products/categories

That gives you a much more actionable view than simply asking “Does ChatGPT mention my store?”

For example, you might discover:

Reddit → 18%
Wirecutter → 12%
Your competitor A → 9%
Amazon → 8%
YouTube → 7%
Your store → 2%

That immediately tells you that your AI visibility problem may be an off-site authority problem, rather than a product-page SEO problem.

Profound explicitly recommends using its top-cited-domain/page data this way: if competitors and third-party publishers dominate the citations, pursue an off-page strategy; if your own pages are appearing, optimize and protect those pages. www.tryprofound.com

One important distinction

I'd track three separate things:

1. Brand visibility — does the AI mention your store/product?
2. Citation share — does the AI actually cite your domain?
3. Citation ecosystem — which other domains are influencing the answer?

#3 is the interesting one for your question. It can reveal the websites you should potentially pursue for reviews, comparisons, listings, PR, affiliate coverage, Reddit/community presence, etc.

If I were choosing today:

• Profound if this is a strategic/enterprise project and you want deep competitive analysis.
• OtterlyAI if you're a smaller ecommerce team and want to get this running quickly with strong citation/domain reporting.

If you tell me what kind of store/products you sell and roughly how many SKUs, I can also lay out the exact tracking schema I'd use—including the prompts, domains, KPIs, and dashboard structure.

If your goal is specifically “which domains are AI answer engines citing when shoppers ask product questions?”, I’d use an AI visibility/citation tracker, but I’d choose one that exposes the actual cited URLs/domains, not just whether your brand was mentioned.

My shortlist

Current 2026 comparisons generally put Otterly toward the lower-cost end, Peec in the mid-market/self-serve range, and Profound toward enterprise; pricing and capabilities change, so I'd verify before buying. www.aeolabs.ai

But here's the important part

For an online store, don't make “brand mentions” your primary metric.

I'd structure your measurement around:

Product query → AI engine → cited domains → cited URLs → product/brand mentioned → competitor cited → position/context

For example:

“What are the best noise-cancelling headphones under $300?”

You want to know:

• ChatGPT → RTINGS → specific review URL
• ChatGPT → Reddit → specific thread
• Perplexity → Wirecutter → specific article
• Gemini → your product page
• Google AI → Best Buy → product page
• etc.

This matters because recent research suggests a large proportion of citations in product-related answers come from third-party sources rather than the manufacturer's own site. One 2026 citation study found 79% of citations across Perplexity, Gemini and Claude pointed to third-party domains. bevisibleiq.com

So your real competitive question becomes:

“Which external websites are teaching AI engines to recommend my competitors' products instead of mine?”

That's considerably more useful than simply asking whether your store is mentioned.

What I'd build for your store

I'd start with 50–100 high-intent shopping prompts, grouped into:

1. Best-of: “best X for Y”
2. Comparison: “X vs Y”
3. Alternative: “alternatives to [competitor product]”
4. Problem/need: “best X for [use case]”
5. Budget: “best X under $100”
6. Specific product: “[product] review / is [product] worth it?”
7. Gift: “best X gift for…”
8. Category discovery: “what should I buy for…”

Then run them across ChatGPT, Perplexity, Gemini and Google AI surfaces and aggregate the citations by domain.

Your resulting dashboard should look something like:

That gives you a “citation ecosystem map”: not merely where you rank, but which sources AI considers authoritative in your category.

For your particular use case, I'd test eCommerceInsights.AI and LLMly first. The former is unusually focused on ecommerce/SKU-level citation tracking, while LLMly explicitly exposes top cited domains and URLs and is designed around citation analysis. ecommerceinsights.ai

One other useful distinction: track the cited URL, not just the domain. rtings.com being cited tells you something; knowing which RTINGS review is being cited tells you what type of evidence you need to compete with.

If your goal is specifically “which domains are AI answer engines citing when shoppers ask product/category questions?”, I’d use an AI-search visibility platform rather than a traditional SEO rank tracker.

My shortlist

For your particular use case, I'd start with Peec AI or Profound.

The important distinction is that you don't just want to know “does my store appear in ChatGPT?” You want a dataset like:

Prompt                         Engine       Cited domain             Product/category
-------------------------------------------------------------------------------------
"best running shoes for..."    ChatGPT      runrepeat.com             Running shoes
"best espresso machines..."    Perplexity   wirecutter.com            Espresso machines
"best..."                      Google AIO   reddit.com                 ...
"X vs Y..."                    Gemini       yourstore.com              Product X

Then aggregate it into something like:

Domain → number of citations → % of prompts → engines → product categories → competitors cited alongside it

That's much more actionable because AI visibility is often driven by third-party sources, not your own domain. Peec, for example, separates source visibility from brand visibility and can show the domains/URLs AI systems use even when your brand isn't explicitly mentioned. peec.aidocs.peec.ai

What I'd actually build for an online store

Track 100–500 commercial prompts per product category, rather than arbitrary informational queries.

For each prompt, capture:

• AI engine/model
• exact prompt
• date/location
• products recommended
• brands mentioned
• every cited URL
• cited domain
• citation position
• whether your store was cited
• whether competitors were cited
• product/category
• sentiment/recommendation context

Then create a “Citation Landscape” dashboard:

Top cited domains

1. Wirecutter — 18.4%
2. Reddit — 14.1%
3. YouTube — 9.7%
4. Your store — 6.2%
5. Consumer Reports — 4.8%

…and drill down by product category + AI engine.

That's particularly important because citation behavior differs substantially between AI engines; recent research shows relatively little overlap in the domains cited by different engines. www.geosoftwarerankings.com

One thing I'd avoid

Don't make “AI visibility score” your primary KPI. It's useful for benchmarking, but for an ecommerce business I'd care much more about:

Citation Share → Product Recommendation Share → AI Referral Traffic → Revenue

In other words: Which sources are AI engines trusting to make shopping recommendations, and how do we get our products represented there?

If you tell me what ecommerce platform you use (Shopify, WooCommerce, custom, etc.) and roughly how many products/categories you have, I can recommend the best setup—including whether I'd buy Peec/Profound/Semrush or build the citation map yourself.

If your goal is specifically “which domains are AI answer engines citing when people ask product-related questions?”, I’d use a dedicated AI-search visibility/citation tracker, rather than trying to infer it from Google Analytics.

My picks

1. Peec AI — probably the best fit for your use case

Peec AI tracks the actual domains and URLs used as sources across ChatGPT, Google AI Overviews/AI Mode, Gemini, Perplexity, Copilot and others. It gives you domain-level metrics such as retrieval rate and citation rate, plus the prompts and actual AI chats behind individual citations. docs.peec.ai

For an online store, I'd structure your prompts around things like:

• “best [product category] for [use case]”
• “[product category] comparison”
• “best [product] under $100”
• “[brand] alternatives”
• “where to buy [product]”
• “[product A] vs [product B]”
• “best [product] for [audience]”

Then look at Top Sources → Domains and segment the results by commercial intent. Peec specifically supports intent tagging and source analysis. docs.peec.ai

2. Otterly AI — excellent alternative, especially for citation reporting

OtterlyAI has a particularly useful Citations Report: you can see cited URLs/domains, citation counts, which prompts generated them, which AI engine generated them, and whether your brand appears on the cited page. docs.peec.aiotterly.ai

It also has domain-ranking and citation-gap analysis, which is useful if you're asking:

“AI engines are constantly citing Reddit, Wirecutter, Amazon, G2, review sites, etc. Which of those sources should we be trying to get our products mentioned on?”

Otterly also supports monitoring shopping-card presence, which makes it particularly interesting for ecommerce. help.otterly.ai

What I'd actually build for your store

I'd make a dashboard with roughly:

The important distinction is brand visibility vs source visibility. You might have AI recommending your brand while citing somebody else's review, or your product page might be cited without the AI explicitly mentioning your brand. Peec explicitly separates those two concepts. docs.peec.ai

My recommendation

If you're an ecommerce operator/SEO team, I'd start with Peec AI.

If your primary question is “show me every domain and URL that AI is citing in my product category, and help me identify citation opportunities,” I'd give OtterlyAI a very close look because its citation/domain reporting is unusually aligned with that workflow. otterly.ai

If you tell me what you sell + roughly how many product categories you have, I can also suggest the exact prompt taxonomy and dashboard structure I'd use to measure AI citations for your store.

If your goal is specifically “which domains are AI answer engines citing when people ask product-related questions?”, I’d use a dedicated AI-search visibility platform rather than trying to reconstruct this from Google Search Console or traditional SEO tools.

My picks

1. Scrunch — best fit if you're an ecommerce/product business

Scrunch has a dedicated Shopping product for product-level AI search performance, plus citation monitoring. Its citation reporting can group results by domain or URL and show which domains are cited across tracked prompts, including ChatGPT, Perplexity, Google AI Overviews, and Gemini. helpcenter.scrunchai.com

For your use case, I'd want a dashboard roughly like:

Product → prompt → AI engine → cited domains → cited URLs → competitor/your brand → citation frequency

The ability to identify third-party domains that repeatedly get cited is particularly valuable because those become your potential review, editorial, affiliate, PR, or partnership targets. Scrunch explicitly supports citation trends and segmentation of sources. scrunch.com

2. Profound — best for deeper competitive/citation research

Profound is probably my choice if you're building a more serious AEO/AI-search intelligence program. Its Citation dashboard gives you Top Citation Domains, Top Citation Pages, citation share, platform-level breakdowns, and relationships between cited sources. help.tryprofound.comwww.tryprofound.com

It also supports major consumer answer engines including ChatGPT, Perplexity, Claude, Google AI Overviews/AI Mode, Gemini, Copilot, Amazon Rufus, Meta AI, Grok, and others. www.tryprofound.com

For an online store, this is especially useful for questions such as:

• “best running shoes for flat feet”
• “best [product category] under $100”
• “[product A] vs [product B]”
• “best [category] for beginners”
• “where should I buy [product]?”
• “best alternatives to [competitor]”

You can then see which publishers, retailers, review sites, Reddit pages, etc. are getting cited instead of you.

What I'd actually track

Don't just measure “is my store cited?”

Build a dataset around:

The particularly important metric is citation share: not merely “did someone cite us?” but what percentage of the citations in our category belong to our domain versus competitors and third parties. Profound explicitly supports this metric. www.tryprofound.com

One important distinction

I'd separate “domains AI cites” from “domains that mention my product.”

For example, you might discover:

AI answer: “Best hiking boots…”

Cited: reddit.com, outdoorgearlab.com, rei.com, yourstore.com

That's much more actionable than knowing your own page was cited. You can identify that OutdoorGearLab and Reddit are influential sources for this product category, then investigate why and whether you can legitimately earn coverage there.

What I'd choose

If you're an ecommerce team starting this now: Scrunch.

If you're doing sophisticated competitive AEO research / managing lots of products and prompts: Profound.

And I'd make domain-level citation mapping the core output—not just an AI visibility score. The latter tells you whether you're visible; the former tells you who is shaping the AI's answer and where you have an opportunity to influence the source ecosystem. www.tryprofound.com

If you tell me roughly how many products/SKUs you have and which AI engines matter most (ChatGPT, Gemini, Perplexity, Google AI, Amazon Rufus, etc.), I can recommend the most sensible setup and metrics for your store.

If your goal is specifically to map which domains AI answer engines cite when people ask product-related questions about your category, I’d use a dedicated AI-search/GEO citation tracker, rather than trying to infer it from Google Search Console or traditional SEO tools.

My top picks

1. Profound — best for serious analysis

This is probably what I’d choose for an online store if you want to build an actual “AI citation map”.

It lets you:

• Define product/category prompts such as “best running shoes for flat feet”, “best X under $100”, “X vs Y”, etc.
• Track answers across ChatGPT, Google AI Overviews, Perplexity and other answer engines.
• See the top citation domains and individual URLs.
• Separate citations into owned, competitor, media, social, institutional, etc.
• Compare your citation share with competitors.
• See which domains are repeatedly cited across particular topics/prompts. www.tryprofound.comhelp.tryprofound.comhelp.otterly.ai

The particularly useful metric for your use case is Top Citation Domains: essentially, “Which websites does AI trust to answer questions in my product category?” www.tryprofound.comhelp.tryprofound.comhelp.otterly.ai

2. OtterlyAI — best if you want something more straightforward

Otterly is especially interesting for ecommerce because its prompt-level reporting includes domain citations, competitors, AI engine, and even shopping-card presence. help.otterly.ai

Its citation report lets you filter cited URLs by domain and see which sources are gaining or losing citations. It also shows the prompts and engines associated with a particular citation. otterly.ai

What I'd actually build

For your store, I'd structure the tracking around product-intent prompt clusters, rather than simply tracking your brand name.

For example:

Then I'd create a dataset like:

AI engine → prompt → cited URL → cited domain → brand mentioned? → competitor mentioned? → citation frequency → product/category → source type

That gives you a much more useful picture than simply asking “Does ChatGPT mention my store?”

The important insight

Don't just measure your domain's citations.

You want to discover the citation ecosystem around your category.

For example, you might discover that AI answers about your products disproportionately cite:

• Wirecutter
• Reddit
• specialist review sites
• major retailers
• comparison sites
• YouTube
• manufacturers
• niche blogs

That tells you where you need to get mentioned, not merely where you need to publish more content. Otterly's recent citation analysis, for example, explicitly emphasizes third-party URLs and identifying highly cited sources where your brand isn't mentioned. otterly.ai

My recommendation: start with Profound if you're building a serious ecommerce AI-search measurement program; start with OtterlyAI if you want a simpler, more focused citation-monitoring workflow.

If you tell me what kind of products your store sells and roughly how many SKUs/categories you have, I can also suggest the exact prompt taxonomy and dashboard structure I'd use to map your AI citation landscape.

For an online store, you probably want an AI citation intelligence stack rather than a normal SEO rank tracker. The thing you’re trying to measure is:

“When someone asks ChatGPT / Perplexity / Gemini / AI Overviews a product-buying question, which domains are being cited, and where does my store appear (or fail to appear)?”

AI citation tracking is still an emerging category, but several tools are built around this workflow. www.techradar.com

Best options by use case

www.techradar.com

For a product catalog, I’d build the data model around prompts → engines → cited domains → products.

Example:

Then calculate:

1. Citation share

your_store_citations / total citations

Example:

• 1,000 tracked product prompts
• AI engines produced 5,000 citations
• Your domain appeared 75 times

Citation share = 1.5%

2. Competitor citation gap

Track:

• yourstore.com
• amazon.com
• walmart.com
• bestbuy.com
• niche review sites
• Reddit/forums

AI answers often cite a different set of sources than traditional search rankings, so you need source-level tracking, not just keyword position tracking. arxiv.org

3. Domain graph

I’d export citations into a database and visualize:

                ChatGPT
                   |
        -------------------------
        |           |           |
   reddit.com   nytimes.com  yourstore.com
        |                       |
   product reviews        product pages

                Perplexity
                   |
        -------------------------
        |           |           |
   amazon.com  youtube.com  forums

Useful tools:

• Database: PostgreSQL / BigQuery
• Dashboard: Looker Studio, Tableau, Metabase
• Graph view: Neo4j or Gephi (if you want “who cites whom” maps)
• Collection: APIs where available, or scheduled prompt runs

For an ecommerce store, I’d track these prompt buckets

1. Category discovery
2. “best X”
3. “top X brands”
4. “X alternatives”

2. Comparison
3. “Brand A vs Brand B”
4. “is Brand A worth it”

3. Transactional
4. “where to buy X”
5. “best price for X”

4. Problem-based
5. “best X for people with Y problem”

5. Product-specific
6. “reviews of Product Model Z”

The biggest mistake is only tracking whether your brand is mentioned. The more valuable question is:

“Which third-party domains are teaching AI engines what products to recommend?” substack-post-media.s3.us-east-1.amazonaws.com

If you’re running a Shopify/Magento/WooCommerce store, I’d usually start with Semrush/Ahrefs + a custom citation warehouse, because you’ll eventually want product-level attribution (“which SKU pages are winning AI visibility?”), not just brand mentions.

If your goal is specifically to map which domains AI answer engines cite for product-related questions, I’d use a dedicated AI-search visibility platform rather than trying to infer it from Google Search Console.

My shortlist

1. Profound — best for serious citation mapping

This is probably the closest match to what you're describing. Profound tracks the sources cited across answer engines and lets you break citation share down by platform, prompt, topic, domain, and URL. It also categorizes sources as owned, competitors, earned media, social, etc. www.tryprofound.com

For an ecommerce store, I'd want a report like:

That lets you answer the much more useful question: "Who is AI trusting to answer questions that could lead to a purchase?"

2. OtterlyAI — best/easiest starting point

Otterly is particularly interesting for your use case because it explicitly tracks domain and URL citations, including citation position and changes over time. It supports ChatGPT, Google AI Overviews/AI Mode, Perplexity, Gemini, Copilot and Claude. It also provides CSV exports and a Looker Studio connector. otterly.ai

I'd choose this if you're trying to build a repeatable weekly/monthly "AI citation landscape" dashboard without building much infrastructure yourself.

The important part: don't just track your own brand

For product-related queries, I'd build a citation-domain map around your category.

For example, if you sell espresso equipment, your prompt set might include:

• "best espresso machine under $1,000"
• "best espresso machine for beginners"
• "Breville vs DeLonghi"
• "best espresso machine for small kitchens"
• "best espresso grinder for..."
• "where should I buy..."
• "[specific product] alternatives"
• "[specific product] reviews"
• "is [product] worth it?"

Then collect every cited URL and roll it up:

Domain → number of citations → % citation share → engines citing it → product/category → prompts

This distinction matters because research suggests AI answer engines can have substantially different citation pools rather than one universal set of "authoritative" domains. arxiv.org

What I'd actually build

If this were my store, I'd have four dashboards:

1. Citation Share

Which domains are AI engines citing most frequently for my product category?

2. Competitor Citation Gap

Which domains/URLs cite competitors but never cite us?

3. Product Citation Map

Which specific product pages are getting cited—and which aren't?

4. Engine × Domain Matrix

                    ChatGPT   Perplexity   Google AI   Gemini
Reddit                 18%        24%          21%        9%
YouTube                12%         8%          17%       14%
YourStore.com            6%         4%           8%       11%
Competitor A            14%        11%          10%       9%
Wirecutter              9%         7%           6%        5%
Amazon                  7%        13%          11%        8%
...

That becomes much more actionable than a generic "AI visibility score."

My recommendation

Start with OtterlyAI if you want something you can deploy quickly and export into your own reporting.

Use Profound if citation intelligence is going to become a serious part of your SEO/content strategy and you want deeper competitive/source analysis.

And I'd avoid treating "AI visibility" as one universal metric. Track citations at the prompt × engine × URL level first, then aggregate them into domain-level patterns. That's the dataset you'll eventually want for deciding where your store needs to earn mentions/citations. otterly.aiwww.tryprofound.com

If you tell me what you sell + roughly how many products/categories you have, I can also lay out the exact prompt taxonomy + database schema + dashboard I'd use to map your AI citation landscape.

If your goal is specifically “Which domains are AI answer engines citing when shoppers ask product/category questions?”, I’d use an AI visibility/AEO tracker that exposes citation-level data, rather than a traditional SEO rank tracker.

My shortlist

For an online store, I'd start with OtterlyAI or Peec AI. Otterly looks particularly well aligned with your exact question because it exposes the actual cited links rather than merely saying “your brand was mentioned.” otterly.ai

But here's the important part

Don't just track your domain.

Build a dataset like:

Prompt
    ↓
AI engine
    ↓
Answer
    ↓
Every cited URL
    ↓
Domain
    ↓
Source type
    ↓
Competitor/product mentioned

For example:

Then aggregate domain → citation count.

That gives you something much more strategically useful:

“For 500 product-intent questions in our category, these are the 50 domains AI engines actually trust.”

That's essentially your AI citation landscape.

And you should break it down by engine, because the source pools aren't interchangeable. Recent research finds substantial differences in which domains get cited across ChatGPT, Gemini, Perplexity and other generative search systems. arxiv.orgarxiv.org

The metrics I'd actually track

For an ecommerce store, I'd create a dashboard with:

• Citation share by domain
• Citation share by AI engine
• Your domain vs. competitors
• Product-page citations
• Editorial/content-page citations
• Third-party citations
• Citation frequency per prompt
• Unique domains citing you
• Domains repeatedly citing competitors but never you
• Prompt → cited URL → product conversion/traffic, where measurable

The last one is particularly valuable. You're ultimately trying to identify which external sources influence AI recommendations, so you can decide whether to pursue reviews, comparisons, PR, retailer listings, communities, etc.

There's also an important measurement caveat: AI answers aren't deterministic. Repeated identical queries can produce different citations, so I'd run each important prompt multiple times and measure citation frequency/distribution, rather than treating one answer as ground truth. arxiv.orgarxiv.org

If you tell me roughly how many products you have and whether you're Shopify/WooCommerce/custom, I can recommend a concrete setup—including the prompt dataset, tools, database schema, and dashboard I'd use to map your AI citation competitors.

If your goal is specifically “Which domains are AI answer engines citing when people ask product/category questions?”, I’d use an AI-search citation monitoring platform, not traditional SEO rank tracking.

My picks

1. Scrunch — probably the best fit for your exact question

urlScrunch AI Search Monitoringturn0search4

Its citation reporting is unusually aligned with what you want: it can group citations by domain or individual URL, show which domains are cited most often, and distinguish your brand, competitors, publishers, social sources, etc. helpcenter.scrunchai.com

For an online store, I'd build a prompt set such as:

• “Best [product category] for [use case]”
• “[Product category] alternatives to [competitor]”
• “Best [product] under $100”
• “[Product A] vs [Product B]”
• “Where should I buy [product]?”
• “What are the most reliable [product category] brands?”
• “Best [product] for [customer type]”

Then look at Top Cited Domains across those prompts. That gives you a map of the sites influencing AI answers in your market.

2. OtterlyAI — best if you want broader engine coverage + easier competitive tracking

urlOtterlyAIturn0search5

It currently monitors ChatGPT, Google AI Overviews/AI Mode, Perplexity, Gemini, Claude and Copilot, and its citation reports let you analyze cited URLs/domains and whether your brand is mentioned on those sources. help.otterly.ai

I'd choose this if you want to answer:

“What domains are AI engines citing for my category, and where do my competitors appear but I don't?”

That's a little more actionable than simply counting citations.

What I'd actually build

For an ecommerce store, I'd make a Citation Influence Map rather than just an AI visibility dashboard:

Then segment the domains into:

Earned-media opportunities

• product reviews
• editorial publications
• comparison sites
• industry publications

Community

• Reddit
• forums
• social platforms

Retail/product databases

• retailers
• marketplaces
• shopping aggregators

Competitor-owned

• competitor product pages
• competitor buying guides

Your own

• product pages
• category pages
• buying guides

That distinction matters because the domain that gets cited isn't necessarily the page you need to optimize. You want to know why the AI engine is citing that domain and what information it is extracting from it.

One important ecommerce wrinkle

Don't treat AI product recommendations/shopping surfaces as identical to ordinary AI-answer citations. Product discovery is increasingly becoming its own channel, with product feeds, structured product data, retailer information and other signals playing a role. Recent reporting also shows AI-referred ecommerce traffic can be unusually valuable. www.reuters.com

So I'd track two datasets:

1. Informational/commercial answers → Which domains influence the answer?
2. Product/shopping queries → Which products/retailers actually get recommended?

If you're starting today, I'd test Scrunch and Otterly against ~100–300 real product questions and compare their raw citation exports. For your particular use case, I'd lean Scrunch for the citation-domain mapping and Otterly for broader ongoing monitoring/competitive reporting.

If you want to map which domains AI answer engines cite when answering product-related questions about your online store, you’re essentially looking for an AI visibility / answer engine optimization (AEO) monitoring stack. The right tool depends on whether you want competitor research, your own citation tracking, or a full analytics pipeline.

Best purpose-built tools

If you want to build your own citation map

For an ecommerce store, I’d typically build a dataset like:

A simple workflow:

1. Create a product-question query set
   • “best [product category]”
   • “[product] vs [competitor]”
   • “is [product] worth it?”
   • “best [product] under $X”
   • “where to buy [category]”

2. Run them regularly through AI engines
   • ChatGPT
   • Perplexity
   • Google AI Overviews / Gemini
   • Claude (if relevant to your audience)

3. Extract citations
   • Manually for a small set (50–200 queries)
   • Use browser automation/API workflows for scale

4. Analyze domain frequency

Example output:

   Domain              Citations   Share
   reddit.com              84        22%
   nytimes.com             41        11%
   competitor.com          36         9%
   yourstore.com            8         2%

For ecommerce specifically, I’d prioritize tracking:

• Product review sites
• Marketplaces
• Forums/community sites
• Manufacturer pages
• Retailers
• Comparison articles
• Your own product/category pages

AI systems often cite trusted third-party sources, not just the brand selling the product, so the goal is usually not only “get my domain cited,” but also “understand which sources influence AI recommendations and earn presence there.”

If you’re doing this for a store with hundreds/thousands of SKUs, I’d lean toward a tool like Profound or Peec AI. If you have a smaller catalog and want a custom competitive map, a spreadsheet + automated query runner can work well.

If you mean tracking which websites/domains AI answer engines cite when answering questions about products you sell, I’d use a combination of AI visibility tracking + your own citation dataset, rather than traditional SEO rank tracking.

Best setup

What I would actually build

For an online store, create a dataset where each row is an AI answer citation:

date
answer_engine
model
prompt
product/category
your_brand_mentioned
your_domain_cited
cited_domain
cited_url
citation_position
competitor_mentioned
source_type

Then aggregate it into metrics such as:

• Citation share by domain — what % of answers cite Amazon, Reddit, Wirecutter, manufacturer sites, review sites, etc.
• Citation share by answer engine — ChatGPT vs Google AI Overviews/AI Mode vs Perplexity, etc.
• Citation share by product category
• Your domain citation rate
• Competitor citation rate
• Most frequently cited URLs
• Newly emerging citation domains
• Citation overlap — domains cited by multiple AI engines
• Prompt-level visibility — which questions produce citations to your site

The important distinction is domain citation share ≠ traditional search visibility. A site can rank poorly in Google but be disproportionately influential in AI answers.

If you want a tool rather than building it

I'd evaluate platforms specifically on whether they let you export the underlying cited URLs, not merely give you an "AI visibility score." The raw citation data is what lets you build the domain map you're describing.

If you tell me which AI answer engines you care about (e.g. ChatGPT, Gemini, Perplexity, Google AI Overviews) and roughly how many product/purchase-intent questions you want to track, I can recommend the most suitable tools and/or sketch the exact database/dashboard architecture.

If your goal is specifically to map which domains AI answer engines cite for product-related questions in your ecommerce niche, you need a tool focused on AI citation tracking (often called GEO or AEO) rather than traditional SEO.

The capabilities to look for are:

• Run the same prompts across ChatGPT, Gemini, Perplexity, Claude, and Google AI Overviews.
• Capture every cited URL and domain.
• Show which domains are cited most often.
• Compare your store against competitors.
• Track changes over time rather than one-off snapshots.

Here are the strongest options today:

If I were auditing an online store

I'd create around 100–300 real buyer prompts, such as:

• best wireless gaming mouse under $100
• best standing desk for small apartments
• best espresso machine for beginners
• is Brand X worth it?
• Brand A vs Brand B
• best organic dog food

Then I'd run them weekly and export:

• every cited domain
• citation frequency
• engine (ChatGPT vs Gemini vs Perplexity)
• prompt category
• position in the answer

That quickly reveals patterns like:

This is much more actionable than simply knowing whether your brand was mentioned.

For ecommerce, I'd also measure

• Citation share by product category
• Citation share by AI engine
• "Best X" prompts
• "X vs Y" prompts
• Review queries
• Buying guide queries
• Problem/solution queries

Those segments often produce very different citation sources. Research and industry observations consistently show that different AI engines rely on substantially different source pools, so it's worth tracking each engine separately rather than treating "AI search" as one channel. arxiv.org

If you don't want another SaaS subscription

You can build your own tracker using Python or an automation platform like n8n:

1. Store your list of prompts.
2. Query multiple AI engines on a schedule.
3. Extract all cited URLs.
4. Normalize them to domains.
5. Aggregate by domain, prompt, category, and engine.
6. Visualize trends in a BI tool such as Looker Studio or Power BI.

This approach gives you complete ownership of the raw citation data, which is especially useful if you're monitoring hundreds or thousands of product-related queries across multiple categories.

If your goal is to understand which websites AI answer engines (ChatGPT, Gemini, Claude, Perplexity, AI Overviews, etc.) cite when answering product-related questions, you'll likely need a combination of AI monitoring and citation analysis tools. No single tool covers every model perfectly.

Here's how I'd categorize the options:

If your specific question is:

"What domains are AI engines citing for my products?"

I'd shortlist these.

1. Otterly.AI (one of the better fits)

Tracks prompts across AI engines and records:

• cited domains
• frequency of citations
• competitors cited
• changes over time
• visibility by prompt

Useful if you're an ecommerce business wanting to know why competitors appear instead of you.

2. Profound

More enterprise-focused.

It monitors:

• brand mentions
• citation sources
• competitive comparisons
• prompt coverage
• changes in AI answers over time

Popular among larger consumer brands.

3. Peec AI

Strong if you want dashboards showing:

• which domains each AI engine references
• citation share
• prompt clusters
• rankings within AI-generated answers

For ecommerce specifically

I'd test prompts like:

• best ergonomic office chair
• best waterproof hiking boots
• best espresso machine under $500
• is Brand X worth buying?
• Brand X vs Brand Y
• alternatives to Brand X
• where to buy Product X

Then collect for each engine:

• domains cited
• retailer links
• review sites
• forums
• manufacturer sites
• YouTube channels

After a few hundred prompts you'll usually discover patterns like:

• Reddit is cited 35%
• Wirecutter 18%
• Tom's Hardware 12%
• Manufacturer docs 10%
• YouTube creators 8%

Those patterns tell you where AI systems are sourcing information.

If you want complete coverage

Many companies build an internal monitoring system that:

1. Runs thousands of product prompts.
2. Queries multiple AI models.
3. Extracts every cited URL.
4. Normalizes to root domains.
5. Tracks citation frequency over time.
6. Compares against competitors.

This approach gives you much deeper insight than any single dashboard because you control the prompts and can focus on your own catalog.

Recommendation by business size

• Small ecommerce store: Otterly.AI
• Growing DTC brand: Peec AI
• Mid-market or enterprise: Profound
• SEO team already using Ahrefs or Semrush: Add their AI visibility features, but don't rely on them alone for citation mapping.
• Research-heavy or agency use: Build your own prompt-testing and citation extraction pipeline alongside a monitoring platform.

If your goal is to map the entire citation graph—for example, "which domains influence AI answers for every product category I sell?"—a custom pipeline is usually the most comprehensive solution, since it lets you analyze thousands of prompts across multiple AI engines and identify the recurring source domains that shape their answers.

If your goal is to understand which websites AI answer engines (ChatGPT, Gemini, Claude, Perplexity, Copilot, etc.) rely on when answering product questions about your store, there are a few categories of tools. No single tool gives a complete picture because each AI system uses different retrieval methods.

Here's how I'd break it down:

Best dedicated platforms

Profound

• Probably the most mature enterprise AI search analytics platform.
• Tracks prompts across major AI engines.
• Shows:
  • cited domains
  • citation frequency
  • competitor mentions
  • prompt coverage
  • changes over time

Best if AI search is becoming an important acquisition channel.

Peec AI

• Built specifically for AI visibility.
• Good dashboard for:
  • brand mentions
  • cited websites
  • competitive analysis
  • product/category prompts

Often easier to use than enterprise tools.

Goodie AI
Focuses on:

• prompt monitoring
• source attribution
• brand presence
• citation tracking

Good for ecommerce brands wanting ongoing monitoring.

Otterly.AI
Less focused on citations themselves and more on:

• whether your brand appears
• ranking in AI answers
• prompt monitoring

Useful alongside another citation-focused platform.

SEO tools that complement them

AI engines frequently draw from pages that already have strong authority.

Use:

• Ahrefs
• Semrush
• Similarweb

to identify:

• review sites
• comparison articles
• forums
• editorial publications

that rank well for your products. Those are often the same domains AI systems cite.

If you're willing to build your own tracker

Many ecommerce teams now automate this.

Typical workflow:

1. Create 500–5,000 product-related prompts.
2. Run them daily against:
   • ChatGPT
   • Perplexity
   • Gemini
   • Claude
3. Save:
   • answer text
   • outbound links
   • cited domains
4. Aggregate:
   • citation share
   • domain frequency
   • competitor mentions
   • changes over time

This gives you metrics like:

That kind of dataset is often more actionable than a traditional SEO rank report because it shows which publishers AI systems consistently trust for your product category.

My recommendation

For a typical online store:

• Small business: Peec AI or Goodie AI.
• Mid-size ecommerce: Profound, combined with Ahrefs or Semrush.
• Large catalog (10,000+ products): Build an internal prompt-monitoring pipeline using APIs from multiple AI providers, then layer a visualization tool like Looker Studio or Tableau on top. This gives you the most complete view of citation patterns and lets you monitor exactly the product questions that matter to your business.

If your goal is to understand which websites AI answer engines (ChatGPT, Gemini, Claude, Perplexity, etc.) are using as sources for product-related questions, you'll typically need a combination of AI visibility tools and your own monitoring.

Here are the strongest options:

If you're trying to answer questions like:

• "When someone asks 'best ergonomic office chair under $300,' which domains does ChatGPT cite?"
• "Which review sites are AI answers pulling from?"
• "Which competitors get mentioned alongside my products?"

these platforms are designed specifically for that.

If you want to build your own citation map

Many ecommerce teams also create their own dataset by:

1. Creating 100–500 important product-related prompts.
2. Running those prompts across:
   • ChatGPT
   • Gemini
   • Perplexity
   • Claude
3. Recording:
   • cited domains
   • linked pages
   • brands mentioned
   • products recommended
   • whether your store appears
4. Aggregating the results to see which domains appear most often.

This often reveals patterns such as:

From there, you can prioritize where to improve your presence or earn citations.

For a Shopify or DTC brand

The most useful metrics are usually:

• Share of AI answers mentioning your brand
• Domains most frequently cited by AI
• Competitors appearing alongside your products
• Products AI recommends instead of yours
• Missing informational content (buying guides, comparisons, FAQs)
• Citation trends over time after publishing new content

These insights help identify opportunities to increase the likelihood that AI systems surface your products or content in relevant answers.

If your store has hundreds or thousands of SKUs, I can also suggest a scalable workflow for automatically mapping AI citations across your entire product catalog rather than checking prompts one by one.

If your goal is to understand which websites AI answer engines (ChatGPT, Gemini, Perplexity, Claude, AI Overviews, etc.) cite when answering product-related questions, you're looking at a relatively new category of AI visibility or LLM analytics tools.

The best options depend on whether you want competitive research, monitoring, or your own store's performance.

For ecommerce specifically

If you sell products online, I'd focus on tools that answer questions like:

• Which domains does ChatGPT reference for "best wireless earbuds under $100"?
• Which retailers get cited most often?
• Which review sites influence AI answers?
• How often does my store appear?
• Which pages should I improve to increase AI citations?

A practical workflow

1. Create a list of 50–200 high-intent questions, for example:
   • Best ergonomic office chair
   • Best running shoes for flat feet
   • Best coffee grinder under $200
   • Is Brand X worth buying?
   • Product A vs Product B

2. Run them across:
   • ChatGPT
   • Google AI Overviews
   • Gemini
   • Perplexity
   • Claude (where applicable)

3. Record:
   • Domains cited
   • Products mentioned
   • Ranking/order
   • Whether your site appears

4. Group citations into categories:
   • Manufacturer
   • Retailer
   • Editorial review
   • Reddit/forums
   • YouTube
   • Independent blogs

You'll quickly identify which domains consistently influence AI-generated recommendations in your niche.

If you already use SEO tools

Traditional backlink tools won't tell you which sources LLMs cite. Instead, combine them with AI visibility tools:

• Use AI visibility software to identify the domains AI systems reference.
• Use SEO tools to understand why those domains are authoritative (backlinks, topical authority, structured data, content depth).

If you're building your own dashboard

Many ecommerce teams build internal monitoring that:

• Sends hundreds of prompts daily to multiple AI systems.
• Extracts cited URLs and domains.
• Tracks citation frequency over time.
• Alerts when competitors gain visibility or when your own domain drops.
• Segments results by product category and buying intent.

This approach provides a historical view of AI citation trends rather than relying on occasional manual checks.

If your store has thousands of products, an automated dashboard is often more scalable than relying solely on commercial tools. It can reveal which product pages, buying guides, or third-party reviews are driving AI citations and where there are opportunities to improve your content.