ChatGPT answers, unedited

For a university press, I’d prioritize tools that can answer two separate questions:

1. Which of our books/papers/pages are being cited by AI?
2. Which outside academic domains and specific URLs are AI systems relying on instead of us?

That second question is especially valuable for improving discoverability.

My shortlist

My pick: Profound + Scrunch

For a university press, I'd actually demo both Profound and Scrunch rather than choosing solely on generic "AI visibility" scores.

Profound looks particularly strong for an organization that wants to turn citation data into an editorial/content strategy: it categorizes cited sources, shows competitors and third-party sources, and explicitly supports identifying publishers/authors that drive citations. Profound

Scrunch has a particularly relevant workflow for your question: its citation reporting exposes top cited domains → individual URLs → the prompts producing those citations, with filters for platform, topic, country, branded/non-branded queries, etc. Scrunch

That lets your press ask something much more useful than "What's our AI visibility?"

For questions about [field/topic], which scholarly publishers, journals, repositories, universities and individual papers does AI cite—and where are we absent?

How I'd use this for an academic press

I'd build a citation intelligence corpus around your catalogue rather than simply monitoring the press's brand name.

For example, for every major subject area, create prompts such as:

• "What are the foundational works on [topic]?"
• "What are the best recent books on [topic]?"
• "What scholarship should I read to understand [specific debate]?"
• "Which scholars have written about [concept]?"
• "What are the major criticisms of [theory]?"
• "What primary sources are important for researching [subject]?"
• "What are the best academic sources on [historical event]?"
• "[Book/article title] — what other scholarship discusses this?"
• "Compare the major interpretations of [topic]."

Then track the actual cited URLs, not just mentions of your press.

I'd put the resulting data into categories such as:

• Your publications
• Other university presses
• Commercial academic publishers
• Journal platforms
• Institutional repositories
• University domains
• Google Scholar/indexing ecosystems
• Wikipedia
• Government/NGO sources
• Professional associations
• Blogs/news/media
• Reddit/social/community sources

That produces a much more actionable picture of the academic information ecosystem feeding AI answers.

One important caveat

Don't treat an AI citation count as equivalent to a citation in scholarly literature.

AI systems retrieve and cite sources differently, and their citation behavior can be quite noisy. Recent research proposes distinguishing citation selection (the system chooses a source) from citation absorption (the source actually influences the generated answer). arXiv

That's actually a useful framework for a press: you ultimately care not merely whether a book's URL appeared in an answer, but whether the book's scholarship is being used to construct the answer.

There is also emerging evidence specifically relevant to academic publishing: a 2026 study of AI-mediated discovery of academic-library resources found that ChatGPT, Perplexity and Gemini were important referral sources, with structured metadata, stable permalinks and openly accessible resources particularly well positioned for retrieval. arXiv

If I were procuring this for your press

I'd run a 2–4 week bake-off using the same ~100–300 scholarly prompts in Profound, Scrunch, and Ahrefs, and score them on:

• URL-level citation capture
• paper/book-level identification
• domain-level aggregation
• coverage of ChatGPT, Gemini, Perplexity, Claude and Google AI surfaces
• historical trend data
• prompt-level reproducibility
• export/API capability
• ability to distinguish your content from third-party citations
• identification of who is winning citations instead of you
• cost at your catalogue/prompt volume

For your particular use case, Profound would be my first enterprise demo and Scrunch my second. Ahrefs is the obvious third if your press already has an SEO/content team using Ahrefs.

If you tell me roughly how many books/papers/domains you want to monitor and whether you need an API/CSV export, I can narrow this to the 2–3 best options and compare current pricing and capabilities.

There is also emerging evidence specifically relevant to academic publishing: a 2026 study of AI-mediated discovery of academic-library resources found that ChatGPT, Perplexity and Gemini were important referral sources, with structured metadata, stable permalinks and openly accessible resources particularly well positioned for retrieval.

If I were procuring this for your press

• "What are the foundational works on [topic]?"
• "What are the best recent books on [topic]?"
• "What scholarship should I read to understand [specific debate]?"
• "Which scholars have written about [concept]?"
• "What are the major criticisms of [theory]?"
• "What primary sources are important for researching [subject]?"
• "What are the best academic sources on [historical event]?"
• "[Book/article title] — what other scholarship discusses this?"
• "Compare the major interpretations of [topic]."

Then track the actual cited URLs, not just mentions of your press.

I'd put the resulting data into categories such as:

• Your publications
• Other university presses
• Commercial academic publishers
• Journal platforms
• Institutional repositories
• University domains
• Google Scholar/indexing ecosystems
• Wikipedia
• Government/NGO sources
• Professional associations
• Blogs/news/media
• Reddit/social/community sources

That produces a much more actionable picture of the academic information ecosystem feeding AI answers.

One important caveat

Don't treat an AI citation count as equivalent to a citation in scholarly literature.

AI systems retrieve and cite sources differently, and their citation behavior can be quite noisy. Recent research proposes distinguishing citation selection (the system chooses a source) from citation absorption (the source actually influences the generated answer).

That's actually a useful framework for a press: you ultimately care not merely whether a book's URL appeared in an answer, but whether the book's scholarship is being used to construct the answer.

If your goal is specifically “Which of our books/papers/domains are being cited inside ChatGPT, Perplexity, Gemini, etc., and what can we do to increase that?”, I’d separate the market into AI-answer citation trackers and scholarly-impact platforms. They solve different problems.

My shortlist

What I'd actually buy for a university press

1. GetIntel (or a comparable AI-citation specialist) as the experimental layer.
You want a system that records something like:

Query → AI engine → answer → cited URL → cited paper/book → publisher domain → competitor domains → date

That's much more actionable than a generic “AI visibility score.” Current comparisons emphasize that citation tracking and brand/visibility tracking are different jobs: you care about the actual sources AI systems retrieve and cite. getintel.ai

2. Keep/use Altmetric for your existing scholarly-impact infrastructure.
Altmetric is particularly attractive for a press because it can identify attention around individual research outputs, journals, and books, and its Explorer/API can be used for institutional analysis. www.altmetric.com It also ingests conventional scholarly citations from Dimensions, including books and other non-journal outputs. help.altmetric.com

3. Add Overton if policy impact matters.
For a university press, this can reveal a very valuable pathway that ordinary citation counts miss: our book → policy document → policymaker/organization. www.overton.io

The important strategic point

I wouldn't optimize merely for “getting ChatGPT to cite our domain.”

I'd build a citation-source benchmark across perhaps 100–500 questions in each major subject area:

• What sources does ChatGPT cite?
• What sources does Perplexity cite?
• What sources does Gemini cite?
• Which university-press domains appear?
• Which commercial publishers appear?
• Which individual books/papers get cited repeatedly?
• Which pages on your site get cited?
• Are PDFs cited more often than landing pages?
• Does a DOI resolve to a page AI systems can crawl?
• Which external sites repeatedly get cited instead of your pages?
• What characteristics do those winning pages have?

That last question is where the commercial value is. The objective isn't simply measuring AI citations; it's identifying the discoverability characteristics that precede them.

There's also a good reason to measure several engines: recent analyses find substantial differences in which domains they cite, so optimizing for one assistant can give you a misleading picture of overall discoverability. www.geosoftwarerankings.com

One caveat for a university press

Don't assume that a tool claiming to measure “AI citations” has a comprehensive census. These systems generally sample queries and observe responses, while AI answers can vary between runs. So I'd demand that a vendor expose:

• exact queries tested
• engine/model tested
• timestamp
• cited URLs
• citation position
• repeatability/variance
• query volume
• historical data
• export/API
• domain-level and individual-ISBN/DOI-level reporting

That makes the data defensible enough to use in a publisher's discoverability program rather than treating an opaque AI score as fact.

My pick: start with GetIntel + Altmetric, with Overton added if policy/social-impact publishing is important. I'd pilot the AI-citation component against a few hundred of your own titles before committing to an enterprise contract. www.altmetric.comwww.altmetric.comgetintel.ai

If you tell me roughly how many titles your press has and which disciplines you publish, I can narrow this to a 3-tool stack and suggest the metrics/dashboard I'd give your editorial, marketing, and web teams.

If your goal is specifically “which academic papers, publishers, journals, authors, and domains are being cited by ChatGPT/Perplexity/Gemini, and how do we improve our university press’s chances of appearing there?”, I’d separate this into two layers:

My recommendation for a university press

Don't treat this as ordinary citation tracking. Build an AI discoverability observatory around a controlled set of research questions.

For example, every week/month run 100–500 prompts such as:

“What are the most important books on Hawaiian history?”
“What scholarship discusses [topic]?”
“What are the leading academic sources on [topic]?”
“Which books should I read about [topic]?”

Then record:

• Which papers/books were cited
• Which DOIs/ISBNs were cited
• Which publisher domains appeared
• Which journal/repository domains appeared
• Position/rank of each source
• Model and model version
• Exact prompt
• Date/time
• Whether your press's content was mentioned but not cited
• Which competing publishers repeatedly appear

That's much more actionable than simply asking whether your domain has been mentioned.

The important distinction

There are actually three different metrics I'd track:

1. Scholarly citation impact
“How often is our scholarship cited by other scholarship?”

OpenAlex is particularly useful here. Its API can follow both incoming citations and the works referenced by a publication, and it supports publisher/topic/institution analysis. help.openalex.org

2. AI citation visibility
“When an AI answers a relevant question, does it cite our content?”

This requires querying the AI systems themselves (or an AI-visibility service). Conventional bibliographic databases cannot tell you this.

3. AI-source authority
“Which domains does AI preferentially use when answering questions in our subject areas?”

This is arguably the most interesting metric for a press. You can aggregate AI answers to discover that, say, jstor.org, a particular university repository, a competitor's catalog, Wikipedia, or your own domain disproportionately supplies citations for a particular field.

One caveat about commercial AI-visibility tools

I'd be cautious about treating their numbers as equivalent to actual global ChatGPT usage. AI search results are volatile, and citation distributions can change substantially over short periods. For example, recent analysis found a sharp change in Reddit's share of ChatGPT citations over just a few weeks. www.axios.com

So for a university press, I'd want raw prompt/response observations alongside the dashboard, rather than relying on a proprietary “AI visibility score.”

A particularly good stack

I'd probably use:

OpenAlex → scholarly baseline
↓
Crossref/DOI + ISBN metadata → normalize your catalog
↓
Promptwatch or Profound → commercial AI monitoring
↓
Your own controlled prompt corpus → reproducible research
↓
Dashboard → AI citations by title, subject, domain, competitor, model and time

OpenAlex is especially attractive for the foundation because its catalog covers hundreds of millions of scholarly works and exposes publishers, sources, topics, citations and related works through an API. developers.openalex.orgdevelopers.openalex.org

OpenAlex API documentation

If you tell me roughly how many titles your press has and whether you're primarily interested in books, journals, or both, I can narrow this down to the 3–5 best tools and a concrete monitoring architecture, including approximate cost and what each can actually measure.

If the goal is “which scholarly papers, publisher pages, and competing domains are AI assistants actually citing?”, I’d treat this as an AI-citation intelligence problem rather than a conventional academic citation-tracking problem.

My shortlist

For your particular use case, I'd start with Profound and Rankscale. The distinction is important: you don't merely want to know “Does University Press X appear in ChatGPT?” You want data like:

For questions about 19th-century American history, which domains does ChatGPT cite? Which individual URLs? Which university presses? Which journal articles? Which repositories? How often does our content appear versus competitors?

Rankscale's published feature set is especially aligned with that question because it exposes domain/URL citation patterns and competitor citation share. rankscale.ai

One important caveat for a university press

Don't confuse AI citations with scholarly citations.

Tools such as scite are excellent for analyzing how academic papers are cited in the scholarly literature, but that's a different measurement problem. You want an additional layer measuring AI retrieval/citation behavior.

I'd build your measurement stack around four levels:

1. AI query → cited domain
   • JSTOR
   • yourpress.org
   • competing presses
   • Wikipedia
   • Google Books
   • institutional repositories
   • journal sites, etc.

2. AI query → cited URL
   • Which specific book landing pages or articles are being retrieved?

3. AI query → cited scholarly work
   • DOI, ISBN, author, title, edition/year.

4. AI engine → citation share
   • ChatGPT vs. Perplexity vs. Gemini vs. Claude vs. Google AI Overviews/AI Mode.

That last point matters because the engines don't necessarily cite the same sources. One 2026 analysis found substantial differences in which domains appear across ChatGPT and Perplexity, so measuring only one assistant can give a misleading picture. distribution.studio

I'd also create a publisher-specific prompt corpus

This is probably more valuable than simply monitoring your press name.

For example, for every major subject area you publish in, maintain 50–200 recurring questions:

• “What are the best books on the Harlem Renaissance?”
• “What are the major scholarly interpretations of X?”
• “Which books should I read to understand Y?”
• “What primary sources are available for Z?”
• “Who are the leading historians of X?”
• “What are the most influential books about [topic] published since 2015?”

Then run the same questions periodically across the major AI systems and record:

question → answer → citations → cited URLs → cited domains → authors/books mentioned → competitors → your books/pages → position/order of citation.

That gives you a much more defensible AI discoverability benchmark than a generic “AI visibility score.”

Perplexity's own documentation is also revealing here: its academic-search workflow explicitly supports academic-domain filtering, DOI/paper metadata extraction and citation-chain construction. docs.perplexity.ai

What I'd buy

If I were setting this up for a university press today:

Tier 1 — serious institutional program:
Profound + your existing SEO analytics + scholarly citation data (Scite/Crossref/etc.)

Tier 2 — lower-cost pilot:
Rankscale + a carefully designed recurring prompt set

Tier 3 — build your own:
Use an AI-search API/collection layer to run the prompts and store every cited URL, then analyze domains, books, authors and competitors yourself. This gives you much more control over academic-specific metrics than a generic SaaS dashboard.

The key metric I'd ultimately report to the press director wouldn't be “AI visibility.” It would be something like AI Scholarly Citation Share: the percentage of relevant AI answers in which one of our books/articles/pages is cited, versus competing publishers and source types.

If you tell me roughly how many titles your press publishes per year and which disciplines you cover, I can narrow this to a practical stack and suggest the exact metrics/dashboard I'd use.

Yes. For a university press, I’d approach this somewhat differently from ordinary “AI visibility”/SEO tracking. You want to know which scholarly articles, books, repositories, journals, libraries, and domains are actually being surfaced as sources by AI systems, not merely whether your press is mentioned.

My shortlist

What I'd choose for a university press

Profound + your own scholarly metadata layer would probably be my first choice.

The key is to turn raw AI citations into something like:

AI engine → question/topic → cited URL → cited domain → publication → author → ISBN/DOI → subject → publication year → competitor/own title → citation frequency → citation context

That lets your editorial/marketing team answer much more useful questions:

• Which books and articles from our catalog get cited?
• Which of our authors are being surfaced?
• Which external domains are consistently cited instead of our publications?
• Which university repositories, JSTOR-like platforms, Wikipedia pages, reviews, library catalogs, and scholarly sites appear in AI answers?
• Which prompts/topics produce citations to our books?
• Are AI systems citing the publisher's landing page, a Google Books page, an institutional repository, a review, or an unrelated secondary source?
• Which competitor university presses are disproportionately represented?
• Which citations lead to actual traffic to our catalog?

That last distinction matters. Citation frequency isn't necessarily citation influence. Recent research on AI citation measurement argues for separating whether a source was selected from whether its content actually influenced the generated answer. arxiv.orgwww.scalepost.ai

One particularly important distinction

I'd measure three separate things:

1. AI retrieval

Did an AI system fetch/read our page?

2. AI citation

Did the answer actually link to our page/book/article?

3. AI influence

Did information from that publication materially appear in the answer?

Those aren't equivalent. A CDN/log-based system such as ScalePost is valuable for #1, while prompt-based citation trackers are better for #2 and #3. arxiv.orgwww.scalepost.ai

For your specific use case, I'd build a “scholarly AI citation map”

Rather than tracking only your press's domain, I'd create a controlled prompt set around your subject areas—for example:

• “best books on [subject]”
• “scholars who argue [proposition]”
• “academic research on [topic]”
• “[historical question] primary sources”
• “recommended books for a graduate seminar on [topic]”
• “most influential recent scholarship on [topic]”

Then collect every cited URL and enrich it with Crossref/OpenAlex/catalog metadata.

The output becomes a network:

AI question → AI engine → cited source → domain → author → title → publisher → subject

That is much more strategically valuable to a university press than a generic “AI visibility score.”

There is also evidence that different AI engines have substantially different citation patterns, so I would not make ChatGPT your only measurement target. semai.ai

If I were procuring this today:
Profound for the commercial intelligence layer + ScalePost if first-party crawler data is important + a small internal Crossref/OpenAlex/catalog database to turn those citations into scholarly intelligence. That combination would give a university press something much closer to an “AI scholarly discovery observatory” than an SEO dashboard.

Yes. But for a university press, I’d use a somewhat different stack than a typical brand/SEO team.

The key distinction is between:

1. AI-answer visibility tracking — “When someone asks ChatGPT/Perplexity/etc. about a topic, which domains and books/papers get cited?”
2. Scholarly citation tracking — “Which papers/books are being cited by other scholarly works?”
3. Discoverability diagnostics — “Why is our content not being retrieved, and what should we change?”

No single product is excellent at all three.

My shortlist

The AI-visibility category has matured considerably in 2026: Profound, Peec, Otterly, Scrunch and similar platforms now run controlled prompt sets against multiple answer engines and record mentions, citations, source URLs and competitors. getrefine.ai

1. I'd start with Profound if you have a real analytics budget

Profound is aimed at enterprise teams and provides granular answer-engine analytics rather than simply checking whether a brand was mentioned. Current comparisons put it toward the top for breadth of engine coverage and reporting. getrefine.aiomidsaffari.com

For a press, I'd configure prompts around things like:

• "best books on medieval economic history"
• "scholars who write about X"
• "most important research on Y"
• "what does the literature say about Z?"
• "recommended books for a graduate seminar on X"
• "primary sources for researching Y"
• "recent scholarship on Z"

Then track which publisher domains, book pages, DOI pages, repositories and individual papers appear in the answers.

That gives you something much more useful than conventional SEO rankings: an AI citation share-of-voice for your catalog.

2. Peec AI is probably the best smaller-team alternative

Peec is particularly attractive if you want a clean monitoring/benchmarking system without buying a heavyweight enterprise platform. Current comparisons describe it as focused on share of voice, competitor benchmarking and multi-engine monitoring. getrefine.aiomidsaffari.com

For a press, I'd create separate projects for:

• subject areas
• individual imprints
• major journals/series
• flagship books
• competing university presses
• important scholarly domains

That lets you answer questions such as “Are our books becoming more visible in AI answers about American history?” rather than merely “Does our homepage get cited?”

3. Otterly.ai is where I'd run the pilot

If you're trying to prove the concept before asking the press to fund it, Otterly is appealing because its entry point is relatively inexpensive and it provides scheduled prompt monitoring across AI surfaces. Current 2026 comparisons put its entry pricing around $29/month, although pricing and engine combinations change. getrefine.aiomidsaffari.com

I'd spend a month tracking perhaps 50–100 high-value questions across your strongest subject areas.

The output I'd want isn't just “we were cited.” It's:

Prompt → AI engine → answer → cited URL → cited domain → cited title → position in answer → competing source → date

That dataset becomes surprisingly valuable.

But add Scite for the scholarly side

This is the important part for a university press.

Scite

Scite isn't simply an “AI visibility tracker.” Its strength is the scholarly citation graph: it can show citation context and distinguish supporting, contrasting and mentioning citations. Its current infrastructure also exposes Smart Citation data to ChatGPT, Claude, Gemini and other AI tools through its MCP integration. scite.ai

That makes it particularly interesting for a press because you can investigate the scholarly ecosystem surrounding your publications, while the AI-visibility tools tell you what happens when people ask answer engines questions.

Scite also explicitly has a publisher-oriented product, so I'd talk to them about whether they can provide publisher-level analytics rather than treating it purely as an individual-researcher subscription. scite.ai

And use Dimensions as the institutional baseline

Dimensions

Dimensions gives you conventional bibliometric measures and programmatic access to article-level citation indicators through its Metrics API. It also now has a Dimensions Research GPT that grounds conversational answers in its research database. www.dimensions.ai

That's useful because you can eventually compare:

traditional scholarly impact → AI discoverability → AI citation

For example:

That is a much more interesting publisher dashboard than either SEO traffic or citation counts alone.

The big caveat: don't treat AI citation counts as objective

This is still an experimental measurement problem.

AI answers can change from query to query, and different engines retrieve different sources. Recent research also shows that even the bibliographic-reference capabilities of major chatbots can be surprisingly unreliable, so you don't want to interpret a single generated citation as equivalent to a Crossref/Scopus/Dimensions citation. arxiv.org

I'd therefore measure repeatable visibility, not “the number of times ChatGPT ever cited us.”

For example, run each prompt 5–10 times per month and calculate:

• Citation rate: % of runs citing your domain
• Source share: your citations / all citations
• Position: where your source appeared
• Unique titles cited
• Competitor share
• Engine differences
• Citation persistence
• Newly cited titles
• Lost citations

That produces a defensible AI Discoverability Index.

One particularly useful metric for a press

I'd create:

AI Scholarly Share of Voice

For each subject area, ask a fixed panel of questions and calculate what proportion of the scholarly sources cited by the AI engines belong to your press.

Then compare that against:

• Oxford
• Cambridge
• Harvard
• Chicago
• Princeton
• Yale
• other relevant specialist presses

The goal isn't necessarily to “beat” them. It lets your editorial/marketing/discovery teams identify topics where your scholarship is disproportionately absent from AI-mediated discovery.

My recommended stack

If I were setting this up for a university press today:

Pilot:
Otterly + Scite + Dimensions/Semantic Scholar

Serious institutional program:
Profound + Scite + Dimensions

If budget is constrained:
Peec + Scite

And I would not buy an AI-visibility product solely because it gives you a big “AI visibility score.” The valuable asset is the underlying prompt → answer → cited source → domain → competitor → time-series dataset.

Semantic Scholar is also worth keeping in the workflow because its citation graph includes citation classifications and influence indicators and covers more than 200 million papers. webflow.semanticscholar.org

If your actual goal is to build a university-press dashboard showing which books/papers/domains ChatGPT, Claude, Gemini and Perplexity cite, I can also lay out the exact data schema, KPIs, sampling methodology, and a recommended $500/month vs. $5,000/month stack.

For a university press, you probably want a tool that tracks which URLs/domains and specific books/articles are being used as sources by chat-based AI systems, not just “AI mentions” of your press name. The market is still immature, so I’d combine an AI visibility tracker with your existing scholarly analytics (Crossref, COUNTER, Google Scholar, Dimensions, Scopus, etc.). nicklafferty.com

Here are the strongest options by use case:

For a university press specifically, I’d set up tracking around:

1. Priority scholarly prompts
   • “best books on [field]”
   • “overview of [historical topic]”
   • “key scholarship on [theory/topic]”
   • “recommended readings for graduate students in [discipline]”

2. Citation-level metrics
   • % of prompts where your books/articles appear as sources
   • which competitor presses appear instead
   • which domains AI trusts for your fields
   • which individual landing pages (book pages, chapter pages, repository pages) get cited

3. Metadata improvements

AI systems often rely heavily on structured, crawlable information. Make sure every title page has:

• complete author/editor metadata
• abstracts and tables of contents
• chapter-level pages where possible
• DOI/Crossref links
• schema.org structured data
• accessible full-text previews where licensing allows

4. Create an “AI discoverability dashboard”

I would not measure only “how often AI cites the press homepage.” The more valuable signal is:

• Which books become canonical answers?
• Which authors are invisible to AI despite strong scholarly impact?
• Which competing titles are being recommended instead?

A practical stack for a medium-sized university press:

• Profound or Ahrefs/Semrush → monthly AI visibility monitoring
• Crossref + COUNTER + Google Scholar/Scopus/Dimensions → scholarly impact baseline
• Custom prompt library → your discipline-specific AI citation benchmark
• Spreadsheet/database → record cited URLs, competitors, and interventions

One caution: AI citation tracking is not yet equivalent to citation indexes. A model citing a book does not necessarily mean it has broad scholarly influence; it reflects the model’s retrieval sources, training data, indexing, and retrieval behavior. Treat it as a discoverability signal, not a new impact factor. nicklafferty.com

If you tell me your press size (small specialist press vs. major university press) and whether you care more about books, journals, or institutional brand visibility, I can suggest a more tailored stack.

For a university press, I’d prioritize tools that track the actual URLs and domains cited in AI answers, not just whether your press name is mentioned. That lets you answer questions like “Which books/articles from our catalog are being surfaced?”, “Which competing presses are cited instead?”, and “What external domains are AI systems trusting for this subject?”

My shortlist

If I were buying for a university press, I'd demo Profound and Rankscale first. Profound looks strongest for institutional-scale monitoring; Rankscale is especially attractive if your central question is “what sources/domains are AI systems actually citing?”

One important distinction for a press

Don't measure only “Is Oxford University Press mentioned?” or “Is our domain cited?”

I'd build your dashboard around four levels:

1. Title/article level — which individual books, chapters, journal articles, reviews, landing pages, etc. get cited.
2. Domain level — press.example.edu versus JSTOR, Wikipedia, Google Books, HathiTrust, competing presses, newspapers, blogs, etc.
3. Topic/query level — e.g. 100–500 questions corresponding to subjects in your catalog.
4. Engine level — ChatGPT vs. Perplexity vs. Gemini vs. Claude, because their citation behavior differs substantially. One 2026 analysis found only about 11% overlap in cited domains between ChatGPT and Perplexity, illustrating why single-engine measurement can be misleading. www.geosoftwarerankings.com

For academic publishing specifically, there's also evidence that structured metadata, stable permalinks and openly accessible resources can make scholarly material easier for AI systems to surface. arxiv.org

I'd also track "citation competitors"

This may be more valuable than raw citation counts.

Suppose you track:

“What were the major interpretations of the Haitian Revolution?”

and the AI repeatedly cites Wikipedia, Britannica, a competing university press, JSTOR and three individual scholars—but never your excellent book on the topic.

Your useful metric isn't merely “our book got 0 citations.” It's:

Who got the citations instead, from which URLs, for which questions, and what characteristics do those sources share?

That turns AI monitoring into an actual discoverability program for your press.

One caveat: AI citation measurements are inherently sampling-based. A single run isn't a stable “ranking”; you want a fixed prompt corpus, repeated measurements, engine-specific results, and historical trends. nicklafferty.com

If you tell me roughly how large your catalog is (e.g. 1,000 vs. 50,000 titles) and whether you're looking for a commercial SaaS purchase or something your analytics team could build, I can narrow this to the best 2–3 options and suggest the metrics/data model I'd use for a university press.

Yes. For a university press, I’d look beyond ordinary academic citation databases. The thing you want to measure is closer to “AI citation visibility”: When someone asks ChatGPT, Perplexity, Gemini, etc. a question related to our books/papers/authors, which scholarly sources and domains does the system actually cite?

My shortlist

For your particular use case, I'd pick Scrunch + Scite

The distinction is important:

Scrunch answers:

“What does ChatGPT/Perplexity/etc. cite when people ask questions in our subject areas?”

Scite answers:

“What scholarly literature cites and discusses our publications, and is that citation supportive or contradictory?”

That gives you two very different kinds of discoverability.

Scrunch's citation system can show, for example:

Prompt: “What are the leading books on Reconstruction-era U.S. politics?”

AI cites: JSTOR → 4 times
HathiTrust → 3
Wikipedia → 2
Publisher X → 2
Your press → 0

Opportunity: Your book is relevant, but AI is getting its evidence through third-party pages rather than your own catalog.

That is much more actionable for a press than simply knowing that a book has 47 conventional citations.

One important caveat

Don't interpret an AI-citation dashboard as an exact census of what ChatGPT or another model “knows.” These systems retrieve different sources depending on the query, model, geography, time, and retrieval configuration. Recent research also finds that AI citation behavior can be highly concentrated among a relatively small set of domains. searchengineland.com

So I'd establish a repeatable prompt panel rather than asking the tools to measure everything.

For a press, I'd monitor perhaps:

• 50–200 questions per discipline
• “best books on X”
• “scholars/books about X”
• “introduction to X”
• “primary sources for X”
• “recent research on X”
• questions involving individual authors
• questions involving specific books
• undergraduate/student-oriented research questions
• graduate-level literature-review questions

Then track citation share by domain, individual URL, book, author, and AI platform over time.

What I'd build for a university press

The most useful KPI isn't simply “How many times were we cited?” I'd create a dashboard with:

AI discoverability

• % of relevant prompts where a press title appears
• % where the press's own domain is cited
• citations per title
• citations per author
• citation share by AI platform

Competitive intelligence

• domains most frequently cited instead of yours
• books from competing presses appearing in answers
• third-party pages that repeatedly influence AI answers
• topics where you're authoritative academically but invisible to AI

Actionability

• titles with high scholarly relevance but zero AI visibility
• catalog pages frequently cited but poorly structured
• Wikipedia/JSTOR/review pages that AI repeatedly uses
• third-party sites worth pursuing for coverage/linking
• metadata/content gaps

That last category is where the real value lies. Scrunch explicitly supports grouping citations by domain and URL and identifying third-party citation opportunities, while Peec provides a similar “top sources” view. peec.aihelpcenter.scrunchai.com

Bottom line: If you want something you can buy and start using, Scrunch would be my first evaluation, with Peec as the closest alternative. I'd add Scite rather than trying to make an AI-visibility product double as a scholarly bibliometrics platform.

If your press has a sizable backlist, I'd also seriously consider a custom OpenAlex + AI-query dataset alongside one of these tools. That would let you measure AI visibility at the ISBN/DOI → title → author → subject → domain level, which is much closer to how a university press actually manages its catalog. www.lse.ac.uk

For a university press, you are looking for something closer to “AI citation intelligence” / “generative engine optimization (GEO) monitoring” than traditional bibliometrics. The market is still immature: no tool has complete visibility into every ChatGPT, Claude, Gemini, or Perplexity answer, so the strongest approach is usually a combination of prompt monitoring + web citation tracking + scholarly metadata analytics. openattribution.org

Here are the tools I would evaluate:

aixiv.science

What I would build for a university press

A practical stack:

1. AI answer monitoring

Track a fixed prompt library, for example:

• “What are the best books on [field]?”
• “Which scholars are influential in [topic]?”
• “Recommend university press books about [subject].”
• “What sources should I cite for [research question]?”

Run these weekly/monthly across:

• ChatGPT
• Claude
• Gemini
• Perplexity
• Google AI Overviews (where available)

Capture:

• cited URLs
• cited books/papers
• author mentions
• publisher mentions
• whether the AI cites your landing pages, metadata pages, PDFs, reviews, or third-party references

This is similar to how emerging AI-visibility research measures “citation share” by extracting URLs returned in answers. openattribution.org

2. Scholarly metadata optimization

Pair AI monitoring with:

• Crossref metadata quality checks
• DOI resolution health
• Google Scholar visibility
• OpenAlex coverage
• Semantic Scholar coverage
• library catalog completeness

A book that exists in AI training/search ecosystems as clean structured data has more opportunities to surface.

3. Domain-level citation tracking

Measure:

• How often your domain appears
• Which pages get cited
• Which competing presses appear instead
• Which topics you “own” vs. lose

For example:

Tools I would shortlist for a press

If you have a small analytics team:

1. Otterly.ai
2. Ahrefs
3. Scite
4. Dimensions

If you have an enterprise marketing/communications team:

1. Profound
2. Scrunch AI
3. Semrush
4. Dimensions

If you want a research-grade internal system, I would build a lightweight pipeline:

• Prompt database
• Scheduled API runs
• Citation extraction
• DOI/title matching
• Dashboard in Tableau/Power BI
• Alerts when competitors appear more often than your titles

Important caveat

Do not optimize only for “getting cited by AI.” AI citation behavior is not the same as scholarly citation behavior, and current chatbots can produce incorrect or incomplete bibliographic references. doi.org A press should track AI visibility as a new discovery channel, alongside library holdings, reviews, citations, downloads, and sales.

For a university press, I would probably start with a 90-day pilot:

• 200–500 high-value prompts
• 5 AI systems
• top 1,000 titles
• competitor benchmark against 5–10 presses
• monthly “AI discoverability report”

That will tell you whether your bottleneck is metadata, content structure, authority, or simply that the AI systems are not surfacing scholarly publishers in your subject areas.

Yes. For a university press, I’d approach this a little differently from ordinary “AI visibility” software: you want to know not just whether your press/domain is mentioned, but which individual books, articles, DOI pages, repositories, reviews, and other scholarly domains are being cited in AI answers.

My shortlist

Scrunch is particularly close to what you're describing. Its citation system records the actual webpages cited in AI responses, lets you drill from domain → URL → prompts, and can compare your press with competitors and third-party sources. scrunch.com

Profound is the stronger choice if you're looking for a serious institutional analytics platform. Current comparisons report URL-level tracking across a particularly broad set of AI engines, including ChatGPT, Perplexity, Gemini, Claude, Copilot, Grok and others. nicklafferty.com

Semrush is attractive if your communications/marketing team also needs ordinary search analytics. Its current AI Visibility tooling can show which pages get cited, which topics generate citations, competitors' citation share, and prompt-level results. www.semrush.com

The important caveat for a university press

None of these is really measuring “all citations made by ChatGPT.” They're measuring citations generated when the vendor runs a defined/sample set of prompts through AI systems.

That's important because AI citation behavior varies enormously by prompt and model. For example, recent Semrush research found only 25.6% overlap in cited domains between ChatGPT's low- and high-reasoning modes for the same prompts. www.semrush.com An academic study similarly argues that citation selection and the extent to which a cited source is actually incorporated into the answer should be treated as separate measurements. arxiv.org

So I'd use these tools to answer:

“When people ask AI systems the kinds of scholarly questions relevant to our books, what sources are they citing, and how often are our publications among them?”

rather than:

“How many times was our book cited by ChatGPT worldwide?”

What I'd build for your press

I'd create a prompt corpus around your subject areas and books, rather than simply tracking your press name.

For example:

• “What are the best introductions to [field]?”
• “What books should I read about [historical topic]?”
• “What are the major scholarly debates about [topic]?”
• “Which scholars/books have shaped research on [topic]?”
• “Compare the major interpretations of [topic].”
• “What primary sources should I consult for [topic]?”
• “[Book topic] bibliography for a graduate seminar”
• Questions derived from your table of contents, book descriptions, abstracts and index terms

Then track four things:

1. Your URLs cited — individual book/article pages, not just press.edu.
2. External scholarly URLs cited — university repositories, JSTOR, Project MUSE, journal sites, Wikipedia, reviews, etc.
3. Domains repeatedly winning citations — e.g. jstor.org, cambridge.org, oxfordacademic.com, particular university sites.
4. Citation gaps — prompts where competing books appear but yours doesn't.

Scrunch explicitly supports this kind of domain/URL/prompt analysis, including citation frequency and an “Influence Score” for prioritizing sources. scrunch.com Peec has also published large-scale analysis of 30 million sources across five major AI search platforms, which could be useful for understanding the broader scholarly-source ecosystem. peec.ai

My recommendation

If you're buying one tool: start with Scrunch and Profound demos.

I'd give Scrunch the edge for your particular use case because your question is fundamentally about source intelligence: Which domains and individual URLs are AI systems actually citing? Its workflow is explicitly built around that question. scrunch.com

I'd choose Profound instead if you expect this to become a substantial institutional program involving many subject areas, hundreds/thousands of titles, multiple AI engines, historical trend analysis, and executive reporting.

And if your press already has a substantial Semrush operation, I'd trial its AI Visibility Toolkit before adding another platform; it now combines AI citations with conventional search data and competitor research. www.semrush.com

One additional recommendation: don't abandon conventional scholarly-impact infrastructure. Pair AI-citation tracking with OpenAlex/Google Scholar/Scite/Dimensions/Web of Science-type citation data. AI citations measure discoverability and influence in AI-mediated research, while bibliographic citations measure something quite different. The two datasets could become a particularly interesting dashboard for a university press.

If you tell me roughly how many titles you publish (e.g. 100 vs. 2,000/year), your budget, and whether you need title/ISBN/DOI-level reporting, I can narrow this to a concrete 2–3 tool stack and suggest exactly what metrics I'd put on the press's dashboard.

If your goal is specifically “which of our books/papers/domains are actually being surfaced or cited by ChatGPT, Perplexity, Gemini, etc., so we can improve discoverability,” I would not buy a conventional bibliometrics product alone. You want an AI-search visibility tracker layered on top of traditional scholarly analytics.

My shortlist

The important distinction is that Scite and Altmetric aren't really substitutes for AI-citation monitoring. Scite's strength is the scholarly citation graph: its Smart Citations classify citation contexts as supporting, contrasting or mentioning, across a very large corpus. scite.ai Altmetric is broader online-attention monitoring rather than specifically “ChatGPT cited our paper.” www.altmetric.com

What I'd recommend for a university press

Start with Profound (or Peec/Scrunch) + Scite.

Use the AI-visibility platform to answer:

• When someone asks ChatGPT/Perplexity/Gemini questions in our subject areas, which of our URLs get cited?
• Which publisher domains, university repositories, journals, Wikipedia pages, reviews, etc. are competing with us?
• Which prompts produce citations to our books/papers?
• Which important queries produce zero representation from our catalog?
• Are citations going to the canonical book/article page, a Google Books/Internet Archive page, a review, a repository copy, or somewhere else?
• Which content changes increase citation frequency over time?

Then use Scite to answer a different question:

Is the scholarly ecosystem actually citing our publications, and how?

That's valuable because conventional citations and AI citations can diverge substantially.

One important caveat

Don't treat an “AI citation count” as equivalent to a scholarly citation count. Generative engines retrieve sources dynamically, and different engines can produce very different source sets. A 2026 study of generative-search citations found substantial differences between engines and argued that citation selection and actual influence on the generated answer should be measured separately. arxiv.org

That's particularly important for a university press. You probably want a dashboard with at least four metrics:

1. AI visibility
% of your tracked prompts where your press/domain appears.

2. AI citations
Number of times a specific URL/book/article is actually cited.

3. Citation share
Your citations ÷ all citations for the topic.

4. Scholarly citation impact
Traditional citations + Scite's supporting/contrasting/mentioning context.

I'd also track citation destination. For example:

Book X → 14 AI citations
Publisher page → 2
Google Books → 7
Library/repository → 3
Reviews → 2

That tells your marketing/web team something actionable: perhaps the book is discoverable, but the publisher's canonical page isn't the page the models trust.

For a university press, I'd prioritize these

Best enterprise strategy:
Profound + Scite + your existing Crossref/Google Scholar/Altmetric data

Best scholarly-first strategy:
Scite + a small internally built AI-query monitoring system

Best budget-conscious strategy:
OtterlyAI/Peec + Scite

And I would not rely on a generic SEO rank tracker. AI engines are not simply returning positions 1–10 from Google; recent research suggests that the source-selection process is sufficiently different that traditional SEO visibility isn't an adequate proxy for AI citation visibility. arxiv.org

If you tell me roughly how many titles/DOIs your press has (e.g. 500, 5,000, 50,000) and whether you're looking for a commercial dashboard or something your digital team could build, I can narrow this to the 2–3 strongest options and compare pricing, APIs, supported AI engines, DOI/ISBN-level tracking, and publisher-scale capabilities.

For a university press, I would treat this as AI visibility / AI citation intelligence rather than traditional SEO. The market is still immature: there is no equivalent of Web of Science for “papers cited in ChatGPT answers,” and different AI systems expose different citation behaviors. Studies of chatbot citation quality also show that citation behavior can be inconsistent, so measurement needs to be designed carefully. doi.org

The strongest options today:

My recommendation for a university press

I would build a hybrid system:

1. AI citation monitoring layer

Use a dedicated AI visibility platform (Profound / Peec AI / AthenaHQ) to track:

• Which prompts produce citations to your books, chapters, journal articles
• Which competing university presses appear
• Which domains AI systems prefer
• Citation share by subject area
• Changes over time

2. Scholarly metadata layer

Connect that to:

• DOI records
• Crossref metadata
• ORCID
• Google Scholar
• OpenAlex
• Dimensions / Scopus / Web of Science (if licensed)

Track:

• “AI citations” vs conventional citations
• Which books/papers are discoverable but under-cited
• Which metadata fields correlate with AI retrieval

3. Build your own “AI citation benchmark”

For a press, I would create a monthly test set:

Example prompts:

• “What are the best books on medieval trade?”
• “Who are leading scholars on climate justice?”
• “Recommend university press books about Indigenous archaeology.”
• “What are authoritative sources on constitutional law?”

Run these against:

• ChatGPT
• Claude
• Gemini
• Perplexity
• Microsoft Copilot

Record:

• Was your title cited?
• Was the publisher cited?
• Which URL/metadata page was used?
• Which competitors appeared?
• Was the citation correct?

This matters because AI systems often retrieve and cite sources differently, and academic citation generation can still contain substantial errors. academicworks.cuny.edu

4. Improve the things AI systems can actually retrieve

For a university press, the biggest wins are usually:

• Rich book landing pages (not just catalog PDFs)
• Structured metadata (schema.org, citation metadata)
• Full abstracts and tables of contents
• Author bios and institutional affiliations
• Stable URLs
• Open-access chapters where possible
• Machine-readable citation formats (BibTeX, RIS, CSL JSON)
• Clear subject taxonomy

If I were choosing a stack

Small/mid-size university press

• Semrush or Ahrefs (existing discoverability)
• OpenAlex + Crossref APIs
• A spreadsheet/database of benchmark prompts
• Monthly manual AI citation audits

Large university press

• Profound or Peec AI
• Data warehouse
• Crossref/OpenAlex integration
• Automated prompt testing
• Dashboard showing AI citation share by discipline

The metric I would care about most is not “how often does ChatGPT mention us?” but:

For the research questions where our books should be authoritative, what percentage of AI-generated answers cite our content compared with competing presses?

That is the closest analogue to market share for AI-mediated scholarly discovery.

Yes. For a university press, I’d look beyond ordinary SEO tools and focus specifically on AI citation/source tracking: which prompts produce citations, which individual URLs get cited, which competing domains appear, and how that changes over time.

My shortlist

Current comparisons show that the major platforms now cover ChatGPT, Perplexity and Google AI results, with some also covering Gemini and Claude. nboundmarketing.com

1. My first choice: Profound

Profound

I'd start here if your press wants a serious AI-discoverability intelligence program, rather than simply a dashboard saying "your brand was mentioned."

The important capability is tracking at the prompt → answer → citation/source level. For a press, you could build prompt sets such as:

• "best books on medieval Islamic history"
• "scholarly books about the history of jazz"
• "academic sources on climate migration"
• "who are the leading scholars on [topic]?"
• "[book title] reviews"
• "[author] books"
• "university press books about [subject]"

Then determine which of your books, author pages, reviews, JSTOR/Project MUSE pages, Wikipedia entries, competing presses, journals, etc. are actually being cited.

2. Particularly interesting for a publisher: Scrunch

Scrunch AI

Scrunch is worth a serious evaluation because it goes beyond "is my brand mentioned?" into citation-source dynamics and AI crawler behavior.

Its current research reports that 87.2% of AI citation events in its dataset came from third-party sources, versus only 5.95% from the brand's own site. scrunch.com

That is extremely relevant to academic publishing.

For example, you may discover that a university-press book isn't being cited because its own product page is poor; instead, the AI is getting information about it from:

• Wikipedia
• Google Books
• JSTOR
• Project MUSE
• scholarly reviews
• library catalogs
• journal articles
• author university pages
• other publishers
• newspaper/book-review coverage

That changes the strategic question from "How do we optimize our book page?" to "Which external scholarly ecosystems make this book legible to AI?"

3. Best lower-cost starting point: Peec AI

Peec AI

Peec is probably where I'd start a pilot if you don't want to commit enterprise money immediately. It focuses specifically on AI-search visibility, including mentions, citations, competitors and prompt-level monitoring. Independent 2026 comparisons put it among the stronger accessible options. nboundmarketing.com

One important caveat

Don't treat an AI citation count like a traditional citation count.

AI answers are stochastic: the same prompt can produce different sources at different times. Recent research specifically argues that AI citation visibility should be treated statistically rather than as a fixed number. arxiv.orgarxiv.org Another 2026 study found substantial differences between platforms and distinguishes between a page merely being retrieved/cited and actually influencing the generated answer. arxiv.org

So I'd want your system to report something like:

Book X
120 tracked scholarly prompts
ChatGPT: cited in 18% of runs
Perplexity: 31%
Gemini: 9%
Google AI: 14%
Most frequent competing sources: JSTOR, Wikipedia, Oxford UP, review journal Y
Most frequently cited URL: [specific page]
Citation trend: ↑ 7 percentage points over 90 days

rather than simply:

"AI visibility score: 74."

What I'd actually build for a university press

I'd use Profound or Scrunch + your existing scholarly metadata/analytics, with a controlled prompt corpus.

The prompt corpus is arguably more important than the software. Create perhaps 500–2,000 prompts spanning:

1. Book discovery — "best books about X"
2. Topic research — "scholarly sources on X"
3. Author discovery — "leading scholars of X"
4. Bibliographic queries — "books published about X since 2020"
5. Course queries — "books to teach X"
6. Graduate research — "recommended readings for X"
7. Book-specific queries — title, ISBN, author, subject
8. Citation-seeking queries — "academic sources I can cite for X"

Then track every cited domain and URL, not merely mentions of your press.

That distinction is crucial: research suggests different AI engines have surprisingly different source pools, so optimizing solely for "ChatGPT visibility" can give you a misleading picture of overall discoverability. arxiv.orgarxiv.org

If I were buying today:
Profound for the main enterprise platform → Scrunch as the strongest alternative/companion for publisher-oriented source intelligence → Peec for a cost-conscious pilot.

If you tell me roughly how many titles your press publishes (e.g. 500, 5,000, 50,000+) and your annual software budget, I can narrow this to a 2–3 tool procurement shortlist and specify exactly what I'd measure.

Yes. For a university press, I’d approach this differently from ordinary “AI visibility”/AEO software: you want paper/book-level citation tracking, exact source URLs, domain-level patterns, and repeatable measurement across academic discovery prompts.

My shortlist

If I were evaluating just two, I'd start with Citations.io and Orbilo. Citations.io explicitly tracks ChatGPT, Perplexity, Gemini and Claude and exposes the sources being cited, while Orbilo is particularly focused on the exact URLs winning citations for particular prompts. citations.io

The important distinction

There are actually three different measurements you may want:

1. “Does an AI answer mention our book/paper/author?”
2. “Does it actually link/cite our press's page or the paper itself?”
3. “Which outside domains does AI rely on when discussing our subject?”

The third is particularly valuable for a press. You could discover, for example, that when users ask about Topic X, ChatGPT consistently cites Wikipedia and a particular university repository, while Perplexity cites a handful of journals and specialist websites. That tells you where your metadata, excerpts, reviews, and author profiles need to be discoverable.

And don't assume one AI platform represents the others. Recent research finds substantial instability in AI citation results, meaning a single query/run can give a misleading impression of visibility. arxiv.org

What I'd build for a university press

Rather than simply tracking the press's domain, create a “scholarly AI discoverability” benchmark:

• 100–500 questions corresponding to your strongest subject areas
• Queries such as “What are the best books on [topic]?”, “Who are the leading scholars on [topic]?”, and specific research questions
• Run them repeatedly across ChatGPT, Perplexity, Gemini and Claude
• Record:
  • cited paper/book
  • cited URL
  • cited domain
  • author
  • publisher
  • citation position
  • whether your title was mentioned but not cited
  • competing publishers/domains appearing instead
• Aggregate by title, discipline, author, domain and AI platform
• Track changes monthly rather than treating one snapshot as definitive

That last point matters: AI citation distributions can vary considerably between repeated queries. arxiv.org

One tool I'd add: scite

scite is worth having alongside an AI-visibility tracker. Its job is fundamentally different: it analyzes scholarly citations and their context—whether subsequent work supports, contrasts with, or merely mentions a paper. That can give your editors a much richer picture of scholarly impact, while the AI-visibility tools measure AI-mediated discoverability.

So I'd think of the stack as:

Scite / scholarly indexes → scholarly impact
Citations.io / Orbilo → AI discoverability
Your own prompt corpus → editorial intelligence

One caution: don't equate “AI cited it” with scholarly importance. Research on AI bibliographic retrieval has found substantial reference errors and even fabricated references in some systems. arxiv.org

If your goal is specifically “Which papers/books/domains are ChatGPT and Perplexity citing when answering academic questions, and how does our catalog compare?”, I can also lay out a vendor-by-vendor comparison of 5–8 tools, including approximate pricing, API/export capabilities, academic suitability, and whether they can track individual URLs/DOIs rather than just brands.

For a university press, I’d separate this into two jobs:

1. Measure actual AI retrieval/citation of your pages — which papers/books/pages are being fetched or cited.
2. Measure synthetic visibility — which prompts cause ChatGPT, Perplexity, Gemini, Claude, etc. to cite your content or competitors.

There isn’t one perfect scholarly-publishing product yet, but a few are particularly relevant.

My shortlist

For your particular use case, I'd start with ScalePost + Citare

The distinction matters.

ScalePost can answer something close to:

"Which of our 40,000 scholarly URLs are AI systems actually requesting?"

That's valuable because a publisher has an enormous corpus. You don't want to manually construct thousands of prompts just to discover that AI agents are repeatedly retrieving The Oxford Handbook of X, article Y, and three pages from your journal platform.

Its publisher positioning is unusually relevant: it claims to observe AI crawlers at the CDN level and provide URL-level, first-party measurements rather than estimates based on asking an AI model questions. www.scalepost.ai

Citare, meanwhile, can answer:

"When someone asks an AI system the kinds of questions our readers ask, which scholarly sources does it cite?"

That lets you build a citation-share-of-voice dataset: your press vs. university repositories vs. JSTOR vs. competing presses vs. individual scholars, etc. Its methodology explicitly includes query design, citation parsing and competitor benchmarking. www.citare.ai

One important caveat

Don't equate "AI fetched my paper" with "AI cited my paper."

Those are different signals. A system can retrieve a page, use its contents internally, and not expose that URL in the final answer. Conversely, a citation can appear without the publisher's name being prominently mentioned. Recent research on AI citations has highlighted this "ghost citation" problem. searchengineland.com

For a university press, I'd therefore build a dashboard with at least:

• AI crawler fetches by URL
• AI citations by URL
• Citation rate by AI engine
• Citation rate by discipline
• Citation rate by book/article/journal
• Share of citations vs. competing presses
• Queries producing citations
• Queries where competitors are cited but you aren't
• Newly cited / lost citations
• AI referral traffic
• AI-cited pages that subsequently generate clicks
• Citation persistence over time

And crucially, track the DOI/article/book URL, not merely your domain. A press-wide "AI visibility score" is much less useful than discovering:

This specific 2023 article is repeatedly cited for questions about climate migration, while our newer 2025 article on the same subject is never retrieved.

That gives your editorial, metadata, SEO and marketing teams something actionable.

One more thing I'd investigate

Because you're an academic publisher, I'd also want OpenAlex/Crossref/Google Scholar-style scholarly metadata alongside the AI data. There is already research suggesting that conventional scholarly prominence can influence which papers LLMs surface; one study of environmental-science queries found a strong tendency toward highly cited, established papers. arxiv.org

So the really interesting analysis isn't simply:

"What does ChatGPT cite?"

It's:

"What predicts an academic work being cited by AI?"

For example:

citations + publication age + journal + DOI metadata + open-access status + backlinks + full-text availability + abstract quality + discipline → probability of AI citation

That could become a genuinely useful AI discoverability program for the press, rather than just another marketing dashboard.

If you tell me roughly how many journals/books/articles your press has and whether you control the web/CDN logs, I can recommend a concrete stack (including lower-cost/open-source options) and a measurement schema I'd use for a university press.

If your goal is helping a university press understand and improve how its books, journals, and papers are surfaced and cited by ChatGPT, Claude, Gemini, Perplexity, and similar systems, there are now a handful of purpose-built platforms. The market is still young, so most publishers combine one AI-visibility platform with traditional bibliometrics.

Here's how I'd evaluate them:

These products repeatedly query multiple LLMs, record:

• whether your domain appears as a cited source,
• which pages get cited,
• which competitors are cited instead,
• which prompts trigger citations,
• changes over time.

This is different from conventional citation indexing—they're measuring LLM citation frequency, not scholarly citations. www.tryprofound.com

For the academic content itself

I'd also pair AI monitoring with scholarly citation tools:

• Semantic Scholar
• OpenAlex
• Crossref
• Scopus
• Web of Science

These answer different questions:

• Which papers are influential?
• Which journals cite us?
• Which topics are growing?
• Which metadata is missing?
• Which DOIs are incomplete?

They do not tell you whether ChatGPT is citing your work.

If you publish journals

The workflow I'd recommend is:

1. Monitor AI citation share with Profound or Lighthouse.
2. Track scholarly impact with OpenAlex + Crossref + Scopus/Web of Science.
3. Build dashboards combining:
   • AI citations
   • Google Scholar citations
   • downloads
   • Altmetric attention
   • DOI resolution
   • referral traffic from AI assistants (where available)

If you're technically inclined

Many university presses are beginning to build their own monitoring systems.

Typical architecture:

• A library of 500–5,000 prompts covering each discipline you publish
• Scheduled queries to ChatGPT, Perplexity, Gemini, Claude, etc.
• Extraction of:
  • cited domains
  • cited papers
  • cited authors
  • missing competitors
  • answer quality
• Time-series dashboards (Looker, Tableau, Power BI)

This gives much richer insights than vendor dashboards because you can tailor prompts to your catalog. One important finding from early experiments is that LLM citation patterns are somewhat variable between repeated runs, so sampling prompts multiple times produces more reliable trends. www.reddit.com

For a university press, my recommendation

If budget allows:

• Best commercial choice: urlProfoundturn0search3
• Good lower-cost alternative: urlLighthouseturn0search5
• Scholarly metadata backbone: OpenAlex + Crossref
• Traditional citation benchmarking: Scopus or Web of Science

That combination lets you measure both academic impact and AI discoverability, which are increasingly distinct metrics.

If your goal is helping a university press understand and improve how its books, journals, and papers are cited by ChatGPT, Perplexity, Gemini, Claude, and similar systems, the market is still early. No platform provides complete visibility into every AI response, but several are becoming useful for "AI discoverability" (often called Generative Engine Optimization (GEO) or Answer Engine Optimization (AEO)).

Here's how I'd rank them.

1. Profound

Probably the most mature enterprise platform.

It measures things like:

• Which domains ChatGPT cites
• Share of voice across AI assistants
• Competitor citations
• Prompt-level monitoring
• Changes over time

For a university press, you could monitor prompts such as:

• "best books on constitutional law"
• "recent archaeology research"
• "recommended papers on climate adaptation"

and see whether your journals appear.

Best if your press has dozens or hundreds of titles.

2. AthenaHQ

Strong for organizations that care about:

• citation frequency
• competitive benchmarking
• AI visibility trends
• executive dashboards

Many publishers use similar monitoring approaches because traditional SEO tools don't capture AI answer engines.

3. Peec AI

Less enterprise-oriented.

Useful if you want:

• recurring prompt monitoring
• citation tracking
• visibility scores
• easier setup

A good option for a university press marketing team.

4. Otterly.AI

Focused on repeated testing of prompts across AI systems.

Useful for questions like:

• Does ChatGPT cite our journal?
• Does Gemini recommend our handbook?
• Has visibility improved since last month?

What no software currently does well

This is important.

No commercial product can reliably answer:

"How often does ChatGPT cite paper X globally?"

because ChatGPT responses are personalized, model versions change, browsing behavior varies, and OpenAI does not expose aggregate citation analytics.

Instead, these products repeatedly test large prompt sets and estimate visibility. Research on GEO likewise relies on controlled prompt sampling rather than direct platform data. papers.ssrn.com

For an academic publisher, I'd build a custom system

Many university presses would benefit more from a bespoke pipeline than an off-the-shelf marketing dashboard.

Example workflow:

• Maintain 5,000–20,000 representative prompts by discipline.
• Query ChatGPT, Perplexity, Gemini, Claude, and Google AI.
• Extract:
  • cited domains
  • cited DOIs
  • journal titles
  • publisher names
  • books
• Store results in a database.
• Generate dashboards showing:
  • citation share by imprint
  • citation share by journal
  • trends over time
  • competitor comparisons
  • prompts where your content never appears
  • prompts where your content displaced competitors

This produces insights that generic GEO tools typically don't offer.

Also monitor infrastructure signals

Academic discoverability increasingly depends on whether AI systems can easily consume your content. In addition to citation tracking, monitor:

• DOI resolution success
• schema.org metadata
• Crossref completeness
• abstracts available without login
• machine-readable references
• ORCID coverage
• persistent URLs
• robots.txt and AI crawler policies
• licensing clarity

These factors make content easier for retrieval systems to identify and cite, although they don't guarantee inclusion. Emerging research suggests that clear structure, explicit answers, and well-supported content are associated with higher AI citation rates. papers.ssrn.com

My recommendation for a university press

• Small press (under 200 titles): Peec AI or Otterly.AI plus a custom prompt-monitoring workflow.
• Large university press: Profound or AthenaHQ, supplemented with an internal analytics pipeline.
• Research-intensive university or library consortium: Build a custom GEO observatory that tracks AI citations across major assistants by DOI, journal, subject area, and publisher. This provides much richer evidence than today's commercial dashboards and can directly inform editorial and discoverability strategies.

For a university press, I would not look for a single “AI citation tracker.” The market is still emerging, and the best setup is usually a combination of:

1. AI answer visibility tracking — “Does ChatGPT/Perplexity/Gemini mention our books, authors, journals, or domains?”
2. Scholarly graph/citation analytics — “Are our works connected to the knowledge graph these systems may draw from?”
3. Content discoverability tooling — “Are our metadata, abstracts, landing pages, and structured data easy for AI systems to retrieve?”

A practical shortlist:

My recommended stack for a university press

For a small-to-mid press

• OpenAlex → build a catalog-level scholarly visibility database. developers.openalex.org
• Peec AI or OtterlyAI → track whether AI assistants surface your titles/domains.
• Google Search Console + your web analytics → measure whether AI-driven discovery turns into visits.

For a large university press

• OpenAlex API + Dimensions/Altmetric for scholarly intelligence.
• Profound or Semrush for AI search monitoring.
• Internal dashboard combining:
  • ISBN/title metadata
  • DOI records
  • author authority signals
  • AI citations
  • referral traffic from AI systems

Metrics I would track specifically for a press

• AI citation share: % of sampled research prompts where your books/articles appear.
• Citation quality: whether AI cites the actual publisher page, DOI, repository copy, or a secondary source.
• Competitor share: which university presses appear instead.
• Metadata gaps: books that are highly cited academically but invisible in AI answers.
• Author/entity recognition: whether AI systems correctly associate authors with your press.
• Domain citation frequency: how often your press domain appears as a source.

One caveat: AI citation measurement is noisy because models can produce different sources for the same prompt, so avoid treating a single snapshot as a definitive ranking. Repeated sampling and trend analysis are more meaningful. arxiv.org

If I were advising a university press starting this program, I would probably pilot OpenAlex + an AI visibility tracker for 100–200 high-value titles, then expand once you know which disciplines and content types are being surfaced.

For a university press, there are really two different problems:

1. Measuring when ChatGPT, Gemini, Claude, Perplexity, etc. cite your books, journals, or website.
2. Understanding why they choose those sources so you can improve discoverability.

I'd recommend a combination rather than a single tool.

For a university press, I'd build this workflow

Tier 1: AI citation monitoring

Use a dedicated AI visibility platform (Orbilo, Profound, or Peec AI) to monitor:

• which of your journal articles are cited
• which book landing pages appear
• which DOI pages get referenced
• which competing presses get cited instead

These tools repeatedly ask AI systems the same questions and log the URLs that appear in answers. That's currently the closest thing to "AI citation analytics." orbilo.cobevisible.app

Tier 2: Traditional scholarly metrics

Continue using:

• Crossref Event Data
• Dimensions
• OpenAlex
• Altmetric

These don't tell you what ChatGPT cites, but they reveal which works are becoming influential—often a leading indicator for AI discoverability.

Tier 3: Build your own benchmark

Many university presses now create an internal evaluation harness.

For example, every week automatically ask 500–2,000 prompts such as:

• "Best books on medieval trade"
• "Leading climate adaptation journals"
• "Most influential feminist archaeology texts"
• "Recent scholarship on Indigenous governance"

Then record:

• which press is cited
• which domains appear
• whether DOI pages are linked
• whether OA versions are preferred
• whether publisher pages or institutional repositories are cited

This produces much richer data than commercial dashboards because it reflects your subject areas.

What actually seems to improve AI citations

Emerging research and industry testing suggests AI systems tend to favor:

• descriptive landing pages rather than sparse metadata
• structured metadata (Schema.org, DOI metadata)
• authoritative domains
• pages with clear author, abstract, references, and publication information
• open-access or easily crawlable content
• stable canonical URLs orbilo.cobevisible.app

If your goal is maximizing discoverability

I'd recommend:

• Commercial monitoring: Orbilo or Profound
• Scholarly metadata: Crossref + OpenAlex + Dimensions
• Internal analytics: A custom prompt-testing pipeline that runs weekly across ChatGPT, Gemini, Claude, Perplexity, and Copilot

That combination gives both executive-level dashboards and actionable insight into which books, journals, and subject pages AI systems actually surface over time.

If you're publishing hundreds or thousands of titles, I can also outline an enterprise architecture (using APIs and automated prompt testing) that several large academic publishers are beginning to adopt for AI discoverability measurement.

If your goal is helping a university press understand what ChatGPT, Gemini, Claude, Perplexity, and Google AI actually cite—and then increasing the likelihood that your books, journals, and domains are cited—you're looking for an emerging category often called AI citation monitoring or Generative Engine Optimization (GEO).

The strongest options today are:

These tools generally run predefined prompts against multiple AI systems and report:

• which domains are cited
• which individual URLs appear
• citation frequency over time
• competitors cited instead of you
• prompts that trigger citations
• opportunities to improve structured content and authority signals. rankscale.ai

For a university press, I'd combine commercial monitoring with scholarly metadata

Most commercial tools were built for brands rather than academic publishers. For a press, I'd pair AI citation monitoring with traditional scholarly infrastructure:

• Crossref DOI metadata
• OpenAlex for open bibliometrics
• Semantic Scholar APIs
• Google Scholar monitoring
• Web of Science or Scopus (if your institution subscribes)

That lets you compare:

• traditional scholarly citations
• AI citations
• domains AI trusts
• books vs. journal articles
• OA vs. paywalled content

Build your own AI Citation Observatory

If you have a technical team, an internal system can be more informative than commercial dashboards.

A typical workflow:

1. Generate 500–5,000 representative prompts across your disciplines.
2. Query ChatGPT, Gemini, Claude, and Perplexity regularly.
3. Extract:
   • cited URLs
   • cited domains
   • cited DOIs
   • books
   • journals
4. Store results in a database.
5. Build dashboards showing:
   • citation share by press
   • citation share by journal
   • most-cited domains
   • uncited content
   • changes over time

Many presses are starting to treat this as the AI equivalent of SEO. rankscale.ai

Questions worth answering

For a university press, I'd track metrics like:

• Which journals are most frequently cited by ChatGPT?
• Which books are cited most often?
• Are AI systems favoring OA content?
• Are DOIs being cited or just publisher pages?
• Which repositories (arXiv, PubMed, SSRN, institutional repositories) are preferred?
• Which competing presses dominate citations?
• Which domains consistently appear alongside your publications?

Features I'd prioritize

For scholarly publishing, look for software that supports:

• Prompt libraries by academic discipline
• DOI extraction
• Citation export (CSV/API)
• Historical trend tracking
• Multiple AI models
• Custom prompt scheduling
• Domain-level analytics
• URL-level analytics
• Competitor benchmarking
• API access

My recommendation

For a large university press (e.g., Oxford, Cambridge, MIT Press scale), I'd recommend:

• Profound for enterprise AI visibility monitoring.
• A custom dashboard built on OpenAlex, Crossref, and Semantic Scholar data for scholarly analysis.

For a mid-sized university press, Rankscale or OmniSEO combined with a lightweight internal dashboard offers a good balance of capability and cost. rankscale.ai

One important limitation: today's AI citation tools generally focus on web domains and URLs, not scholarly citations at the level of DOIs, books, authors, or journals. If your objective is to understand how AI systems surface academic literature, you'll likely get the best results by augmenting one of these platforms with your own analytics pipeline tailored to scholarly metadata.