Answers that mention Peec AI

Yes. What you’re looking for is generally called AI citation tracking, LLM visibility tracking, or GEO (Generative Engine Optimization) tracking.

For an ecommerce blog, the useful distinction is whether a tool merely tells you “ChatGPT mentioned your brand” or actually tells you which domains/pages ChatGPT, Perplexity, Gemini, etc. cited in their answers.

Tools worth looking at

• Ahrefs Brand Radar — probably the easiest place to start if you already use Ahrefs. It tracks ChatGPT, Gemini, Perplexity, Copilot and Google AI results, and specifically reports cited domains and cited pages, not just brand mentions. Ahrefs Ahrefs
• Profound — geared more toward serious/enterprise AI-search monitoring. It tracks citations down to the URL level across a broad set of AI engines, so you can investigate exactly which pages are being used as sources. Nick Lafferty
• Frase — a simpler option for content teams. Its AI Visibility Checker looks at ChatGPT, Perplexity and Gemini and reports citations, share of voice and related visibility metrics. Frase
• Searchable — particularly interesting if your goal is researching citation patterns across the web, rather than only monitoring your own brand. It publishes citation datasets for ChatGPT, Google AI Overviews and Perplexity and offers brand monitoring. Searchable
• Otterly.AI / Peec AI / Scrunch / similar GEO platforms — worth considering if you want ongoing prompt monitoring and competitor comparisons; the category has expanded considerably in 2026. Birdeye

For your specific ecommerce use case

I'd structure the tracking around questions such as:

“What are the best running shoes for flat feet?”
“Best gifts for a 10-year-old who likes science”
“Which [product category] is worth buying under $100?”

Then track, for each chatbot:

Ahrefs is particularly aligned with your wording because its current AI visibility reporting explicitly includes the top cited domains and top cited pages associated with AI responses. Ahrefs

One important caveat: these tools generally sample prompts and AI-generated responses rather than having omniscient access to every chatbot conversation. AI results can also vary by query, location, time, model and retrieval context, so citation tracking is best treated as a measurement sample rather than an exact census.

If you tell me roughly how many blog posts/products you have and your monthly budget (e.g. $0, <$100, $100–500, $500+), I can narrow this down to the most practical 2–3 tools for an ecommerce site.

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

My shortlist

• otterly.ai — strongest fit for a local business

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

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

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

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

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

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

For example, if you owned a Pittsburgh roofing company:

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

Then track, for each prompt:

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

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

What I'd choose

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

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

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

Here are the strongest options:

My shortlist by business type

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

Start with:

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

Multi-location business or marketing agency

Consider:

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

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

Look for these metrics:

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

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

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

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

1. otterly.ai — probably the closest fit

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

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

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

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

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

2. peec.ai — strong alternative

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

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

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

My recommendation

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

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

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

If your goal is specifically “which domains are AI answer engines citing when shoppers ask product questions about my store/category?”, you want an AI citation tracking / GEO (Generative Engine Optimization) tool, not a traditional SEO rank tracker.

The main options I’d evaluate:

For an online store, I would structure the measurement like this:

1. Build a prompt set around buying intent

Track prompts like:

• “best [product category]”
• “[product] alternatives”
• “[brand] vs [competitor]”
• “best [product] under $X”
• “where to buy [product]”
• “is [product] worth it”
• “best [use case] product”

The important unit is the prompt → AI answer → cited domains, not just your brand mention.

2. Export citations into a database

Create a table like:

Then group by:

• domain citation share
• product category
• AI engine
• competitor overlap
• URL type (review, marketplace, product page, forum)

3. Watch these domain categories

For ecommerce, the domains AI engines cite often fall into:

• Review publishers
• Reddit/community sites
• Marketplaces
• Manufacturer sites
• Retailers
• Comparison sites
• YouTube/blog content
• Your own product pages

The strategic question becomes: “What sources are shaping the AI answer before the shopper reaches checkout?”

My practical recommendation

For a small-to-medium store:

1. Start with Ahrefs Brand Radar if you already pay for Ahrefs. Ahrefs
2. Add a commerce-focused tracker like LLM Metrix or eCommerce Insights AI if you care about SKU-level visibility. LLM Metrix eCommerceInsights.AI
3. Keep your own spreadsheet/database of prompts and citations so you can analyze trends independently.

If you tell me your platform (Shopify, WooCommerce, Magento, custom) and approximate SKU count, I can suggest a more specific stack and data model.

The strategic question becomes: “What sources are shaping the AI answer before the shopper reaches checkout?”

My practical recommendation

For a small-to-medium store:

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

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

My practical choice

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

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

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

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

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

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

What I’d shortlist

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

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

For example, create a recurring prompt set such as:

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

Then capture, for every AI response:

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

That lets you answer much more useful questions such as:

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

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

My practical recommendation

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

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

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

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

Those are increasingly different forms of discoverability.

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

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

For a university press, you’re looking for AI citation visibility / generative engine optimization (GEO) monitoring, not traditional scholarly citation tracking. The tools are still immature, but a few categories are useful.

Best options to evaluate

1. AI visibility monitoring platforms (closest fit)

These track which domains and pages appear as sources in AI-generated answers.

• Profound — strong for enterprise-style monitoring of AI answers, prompts, competitors, and cited sources. Good if your press has a large catalog and wants dashboards.
• Scrunch AI — focuses on tracking brand presence and citations across generative search experiences.
• Otterly.AI — useful for tracking prompts and whether your pages appear in AI search results.
• Peec AI — lighter-weight monitoring of AI search visibility.

These tools are usually better at answering:

• “Does our press domain get cited?”
• “Which competitors are cited instead?”
• “Which prompts trigger our content?”

They are generally weaker at:

• “Which individual DOI/paper is being cited inside ChatGPT answers?”

AI citation measurement is still a developing field, and different models often cite different source sets for the same question. Temso AI

2. Academic discovery + citation intelligence (pair with AI monitoring)

For a university press, combine AI monitoring with scholarly metadata tools:

• Dimensions — broad research graph, citations, grants, patents, and attention signals.
• Scopus — strong bibliometrics and author/institution analysis.
• Web of Science — traditional citation analytics.
• Crossref — useful for DOI metadata, references, and publisher infrastructure.
• OpenAlex — good open alternative for building custom dashboards.

These won’t tell you “ChatGPT cited this chapter,” but they help identify the works that have authority and are likely candidates for AI retrieval.

For a university press specifically, I would build a stack like this

What I would measure monthly

Create a benchmark of 100–500 prompts such as:

• “What are the most influential books on medieval trade?”
• “What scholarship should I read on climate justice?”
• “Who publishes authoritative works on [field]?”
• “What are the leading academic sources on [topic]?”

Then track:

• % of answers where your press appears
• titles/books/articles cited
• author mentions
• DOI/ISBN visibility
• competing presses cited
• referring URLs/domains
• whether AI cites your own pages vs. external pages

Some current studies suggest AI citation patterns are highly fragmented across models, so optimizing for only one assistant is risky. Temso AI Parse

Also improve the “AI retrieval surface”

For presses, technical discoverability often matters:

• expose complete book metadata (authors, abstracts, TOCs, keywords)
• make chapter-level pages crawlable where possible
• include DOI/ISBN/schema metadata
• provide machine-readable citations (BibTeX/RIS)
• avoid hiding abstracts behind heavy JavaScript or login walls
• maintain authoritative author profiles

My shortlist for a university press pilot

1. Profound or Scrunch AI — institutional monitoring
2. OpenAlex + Crossref APIs — scholarly graph layer
3. A custom prompt benchmark — the most important piece
4. Google Search Console + analytics — to connect AI visibility to traffic

I would not replace traditional bibliometrics yet; I’d add an “AI citation impact” layer alongside COUNTER usage, Crossref events, downloads, and scholarly citations. AI-source tracking is emerging, and current datasets show that citation behavior varies substantially by model and query type. Temso AI proceedings.mlr.press

Also improve the “AI retrieval surface”

For presses, technical discoverability often matters:

If your enterprise content team specifically needs citation frequency + position/rank across multiple AI models, I’d shortlist Scrunch and Peec AI.

Best fits

• scrunch.com — strongest fit if citation intelligence is the centerpiece. It tracks cited URLs/domains, citation frequency and trends, brand presence, response position, share of voice, and competitor visibility. It currently supports ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Claude, Copilot, Grok, and Meta AI. Scrunch Scrunch Help Center

• Particularly enterprise-oriented: SOC 2 Type II, SSO/RBAC, multi-brand/domain/region support, and API access. Scrunch
• Its citation view lets teams drill from domain → specific URL → prompt-level performance, which is useful for editorial/content teams trying to understand which pieces of content actually get cited. Scrunch Help Center
• peec.ai — arguably the cleaner fit if by "rank" you mean where your brand/content appears in an AI answer. It tracks visibility, average position, citation count/frequency, sentiment, and share of voice by model. Peec AI

• Covers ChatGPT, Gemini, Perplexity, Google AI Overviews, Google AI Mode, and Copilot, with additional models available on higher tiers. Peec AI
• It also distinguishes brand mentions from source citations, and exposes citation frequency at domain/URL level. Peec AI
• Daily tracking makes it useful for monitoring changes after publishing or updating content. Peec AI

How I'd choose

One important caveat: AI "rank" isn't equivalent to a Google SERP ranking. ChatGPT and other models don't necessarily return a fixed numbered list, so these platforms generally operationalize rank as things such as position in the generated answer, mention frequency, visibility, or average position. Peec explicitly makes this distinction. Peec AI Peec AI

For an enterprise content organization, I'd start with Scrunch if the core question is "What content and third-party sources are AI models citing, how often, and how is that changing?" I'd start with Peec if it's "How often are we mentioned and where do we appear relative to competitors across each model?"

If you tell me your approximate scale (e.g. 1,000 vs. 100,000 prompts/month, number of brands/domains, and whether you need API/BI integration), I can narrow this down further and include Profound, Otterly, Ahrefs Brand Radar, and other enterprise GEO platforms.

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

Here are the main platforms to evaluate:

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

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

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

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

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

Lighthouse LighthouseIf you want to build your own reporting layer:

• Trackee or another API-first provider

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

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

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

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

If your goal is specifically “how often is my domain being cited by AI assistants, and how has that changed over time?”, I’d look first at peec.ai.

Why it fits:

• Tracks source citations separately from brand mentions—so your domain can be measured even when the AI doesn't explicitly name your brand. Peec AI
• Shows citation/source data at both the domain and URL level, including citation frequency. Peec AI
• Tracks multiple AI surfaces, including ChatGPT, Gemini, Perplexity, Google AI Overviews, Google AI Mode, and Copilot. Peec AI Peec AI
• Data is refreshed daily, which makes it useful for building historical trend lines. Peec AI
• You can segment results by model, country, and prompt, and export/use the data through Looker Studio, API, or CSV. Peec AI Peec AI

Another strong option: Profound

tryprofound.com is particularly interesting if you want deep citation analytics. Its Citations section provides citation share and citation coverage as time-series charts, lets you compare domains, and break results down by platform, topic, region, and other dimensions. Profound Help Center

Profound also explicitly tracks which answer engines cite your content, across the prompts you're monitoring, and categorizes sources such as owned content, competitors, earned media, and social sources. Profound

My practical distinction:

• Peec AI → better fit if you want a straightforward ongoing AI visibility + domain citation dashboard across many assistants.
• Profound → worth considering if your primary job is AEO/GEO research and detailed citation intelligence.

If you tell me your domain and which assistants matter most (ChatGPT, Gemini, Perplexity, Google AI, Claude, etc.), I can also compare the relevant platforms on historical depth, citation-level tracking, pricing, and API/export capabilities.

If your goal is specifically “How often is my domain being cited by AI assistants, and how has that changed over time?”, I’d recommend Peec AI.

peec.ai is particularly well suited because it tracks source citations separately from brand mentions. You can see which URLs/domains are being used or explicitly cited, citation frequency, the AI engines involved, and trends over time. It covers ChatGPT, Gemini, Perplexity, Google AI Overviews/AI Mode, and others. Peec AI Peec AI

My shortlist

• 🥇 Peec AI — Best fit for your exact requirement. It distinguishes domain/source visibility from brand visibility, tracks citations at the URL and domain level, and provides historical/trend data. Peec AI Peec AI
• 🥈 OtterlyAI — Very good alternative if you want detailed citation analytics. It stores AI responses and tracks every cited URL, alongside mentions, rank, sentiment and share of voice across seven AI search engines. Otterly
• 🥉 Profound — Worth considering for a larger enterprise GEO/AI-search program, particularly if you want broader enterprise analytics rather than primarily a citation-monitoring workflow.

For your use case, I'd start with Peec. The important distinction is that you don't merely want to know “Does ChatGPT mention my company?” You want “Is example.com being used as a source, which pages are cited, by which models, and is that citation footprint growing or shrinking?” Peec explicitly supports that distinction. Peec AI

If you tell me your domain + whether you're primarily interested in ChatGPT, Gemini, Perplexity, or all of them, I can also compare Peec vs Otterly vs Profound on historical data retention, URL-level citation tracking, API access, and pricing.

Yes. What you’re looking for is generally called an AI visibility / AI citation tracking platform. These tools run a controlled prompt set across multiple assistants, collect the answers and cited URLs, then aggregate the results into metrics such as citation rate, mention rate, competitor share of voice, and citation share.

For a SaaS marketing team, I’d shortlist:

• tryprofound.com — enterprise-oriented AI search intelligence; useful if you need deep citation/source analysis and reporting across multiple AI engines. Recent industry research is using Profound data across ChatGPT, Perplexity, Gemini, Copilot and Google AI surfaces. Tinuiti
• peec.ai — focused specifically on AI visibility/GEO tracking, including competitor comparisons.
• otterly.ai — more accessible for teams/agencies wanting recurring AI visibility and citation monitoring.
• citationly.ai — explicitly tracks citations, mentions, competitors and share of voice across ChatGPT, Gemini, Claude, Perplexity, Copilot and Grok. CITATIONLY
• cloro.dev — particularly interesting if your SaaS team wants API-accessible raw citation data rather than just a marketing dashboard. It says its API returns parsed citations, source URLs, positions and labels across seven engines, which you can aggregate yourself into SOV. cloro

If you're building a reporting stack

I'd distinguish two use cases:

"Give me a dashboard" → Profound / Peec / Otterly

"Give me the underlying citation dataset so we can pipe it into our own BI/data warehouse" → Cloro or another API-first provider.

For your specific phrase "aggregate citation data from different AI assistants", the API approach is especially attractive. Your canonical dataset could look like:

date × prompt × AI engine × brand × cited URL × citation position × mention × recommendation

Then your marketing dashboard can calculate:

• AI Share of Voice = your brand's appearances ÷ all tracked brand appearances
• Citation Share = answers citing your domain ÷ all answers
• Mention Rate = answers mentioning your brand ÷ total answers
• Citation Position = average position when your domain is cited
• Competitor SOV by prompt category
• Engine-specific SOV — ChatGPT vs. Perplexity vs. Gemini, etc.

That's important because simply combining all assistants into one number can obscure substantial differences between engines. Current AI-visibility methodologies generally recommend retaining the per-engine breakdown. organikpi.com geosoftwarerankings.com

If you tell me your SaaS category, approximate number of prompts, and whether you want an API or just a dashboard, I can narrow this to 2–3 options and compare pricing, API access, engine coverage, and reporting capabilities.

Then your marketing dashboard can calculate:

• AI Share of Voice = your brand's appearances ÷ all tracked brand appearances
• Citation Share = answers citing your domain ÷ all answers
• Mention Rate = answers mentioning your brand ÷ total answers
• Citation Position = average position when your domain is cited
• Competitor SOV by prompt category
• Engine-specific SOV — ChatGPT vs. Perplexity vs. Gemini, etc.

That's important because simply combining all assistants into one number can obscure substantial differences between engines. Current AI-visibility methodologies generally recommend retaining the per-engine breakdown.

If your goal is “which exact prompts cause ChatGPT/Claude/Perplexity/Gemini to cite my how-to guides?”, you want an AI citation tracking / AI visibility platform, not a normal SEO rank tracker. The key feature is prompt → AI answer → cited URL mapping. SolvSpot Scrunch

Here are the strongest options:

For how-to guides specifically, I would prioritize these capabilities:

• Prompt discovery: “What questions are people asking that result in my URL being cited?”
• Citation URL extraction: Not just “your brand appeared,” but “this exact guide was cited.” Scrunch
• Prompt clustering: Group queries like:

• “How do I configure X?”
• “Best way to solve Y?”
• “X vs Y”
• “Troubleshooting X”
• Competitor source comparison: See which guides are replacing yours in AI answers. Scrunch

A practical workflow:

1. Load your important guides into a tracker.
2. Add 50–200 natural-language prompts your audience would ask AI.
3. Run them across ChatGPT, Claude, Gemini, and Perplexity.
4. Export:

• prompt
• model
• cited URL
• citation frequency
• competing cited pages
• Update guides where competitors consistently win citations.

If you are a solo content marketer or small SaaS, I’d start with Peec AI or Otterly.AI. If you have a large content library and need to know exactly which prompts produce citations at scale, look at Profound or Scrunch AI. Honeyb Scrunch

One caveat: there is currently no equivalent of Google Search Console for AI citations; these tools generally work by running tracked prompts repeatedly and analyzing the resulting AI answers, so results are samples rather than a complete log of every user query ever asked. citeflow.io searchmention.com

If your specific goal is “show me the exact prompts/queries where an AI model cited my how-to guide, and which URL it cited,” I’d look at AI-visibility tools rather than traditional rank trackers.

Strong options

• otterly.ai — probably the closest match to your requirement. It lets you monitor exact prompts, see the resulting AI answers, and track the specific URLs cited, including position changes over time. Its Prompt Research feature can also discover prompts from a keyword, brand, or URL. Otterly Otterly AI
• peec.ai — useful if you want prompt/answer monitoring plus competitive analysis and citation visibility across multiple AI search surfaces. Marqeable Conbersa
• tryprofound.com — more enterprise-oriented, with deeper citation intelligence and prompt libraries. It's worth considering if you have a large content library or need sophisticated competitive reporting. Marqeable SEO Counselors
• ahrefs.com Brand Radar — particularly interesting if you're already using Ahrefs, since it connects AI citation data with conventional SEO data and competitor research. Marqeable

The important distinction

There are actually two different questions you may be asking:

1. “Which prompts cause AI to cite my guide?”

→ Prompt-level monitoring is what you want.

2. “What prompts might cause AI to discover/cite my guide that I haven't thought of?”

→ You want prompt discovery/query fan-out in addition to monitoring.

For #2, Otterly is particularly interesting because it explicitly offers AI Prompt Research and Query Fan-Out, alongside URL-level citation tracking. Otterly

One caveat: these platforms generally run/monitor a defined set of prompts; they cannot literally reveal the private internal prompt or reasoning that caused an arbitrary ChatGPT response to cite your page. What they can give you is the observable input → AI answer → cited URL relationship.

For a site publishing how-to guides, I'd test Otterly first and specifically look for: exact prompt, model/engine, full response, cited URL, citation position, and historical changes. That gets closest to the “what exact question made my guide appear?” workflow you're describing. Otterly

Yes. If your developer team wants to pull AI citation/visibility data via API and build its own dashboards, there are several credible options. The best choice depends on whether you want raw citation events or a more complete AI-search analytics dataset.

My recommendation

If your team is actually building the data infrastructure, I'd investigate MentionsAPI first. It is specifically designed around citation extraction: it returns which URL was cited, for which prompt, and by which AI provider, while resolving/deduplicating citations. MentionsAPI

That gives you a relatively clean underlying data model such as:

prompt
├── provider
├── model
├── timestamp
├── answer
└── citations[]
    ├── url
    ├── canonical_url
    ├── domain
    └── position

You can then put that into your own warehouse and build dashboards in Looker, Tableau, Power BI, Grafana, or a custom React app.

If you want less engineering and more pre-computed marketing metrics, I'd look at OtterlyAI. Its API exposes brand reports, prompts, citations, and workspace data, while its analytics layer includes citations, mentions, sentiment and share-of-voice across seven AI engines. Otterly Otterly

For an enterprise-grade analytics layer, Profound is worth evaluating. It is geared toward deeper brand intelligence and competitive/share-of-voice analysis, although API access and pricing are more enterprise-oriented. Toolsolved

The important distinction

I'd separate the market into two categories:

1. Citation extraction APIs

You want:

"For this prompt, what URLs did ChatGPT/Claude/Perplexity cite?"

→ MentionsAPI is particularly interesting.

2. AI visibility platforms with APIs

You want:

"What is our AI visibility score, share of voice, sentiment, competitor position, citation rate, and trend over time?"

→ OtterlyAI, Promptwatch, Peec, or Profound are more appropriate. A recent comparison also identifies Promptwatch as particularly strong for programmatic access to visibility, citation, prompt and competitor data. Toolsolved

If you're building a developer-facing AI citation dashboard, I'd probably shortlist MentionsAPI + OtterlyAI + Promptwatch and compare their actual API schemas, rate limits, pricing, historical-data access, and whether they return the full AI answer alongside citations. That last point can make a huge difference to what your team can build.