ChatGPT answers, unedited

Yes. For a publisher, I’d look beyond conventional SEO software and specifically at AI-search / answer-engine visibility platforms. The goal is not merely “rank in Google,” but to answer four questions:

1. Which of our articles are actually being cited by AI answers?
2. Which competitors/publishers are being cited instead?
3. What characteristics do the cited pages have that ours lack?
4. Can we measure whether changes increase citations and AI-referred traffic?

My shortlist

• scrunch.com — probably the most directly relevant for a publisher. It tracks AI citations at the URL level, showing which specific pages appear in answers from ChatGPT, Perplexity, Google AI Overviews, Gemini and others. It also tracks AI-agent traffic, AI referrals, and gives page-level optimization signals. Scrunch Scrunch Help Center Scrunch Help Center

Best for: an editorial organization that wants to understand which articles are getting cited and why.

• tryprofound.com — a more comprehensive answer-engine intelligence platform. Its citation analysis lets you compare your URLs against competitors and identify the third-party publishers and sources that AI systems rely on most. It also has content/workflow features for acting on those findings. Profound Profound Profound

Best for: a larger publisher with SEO/content strategy teams that want competitive intelligence as well as monitoring.

What I'd actually buy

If your specific concern is “AI is consuming our journalism but sending us fewer visitors, so how do we maximize the chance that our journalism gets attributed?”, I'd start with Scrunch or Profound, rather than simply adding another traditional SEO suite.

The important distinction is that these tools can show you the citation chain. For example, you might discover:

Your article → rarely cited
Competitor article → cited frequently
Reddit discussion → cited frequently
Government/academic source → cited frequently

That gives your editorial/SEO team something actionable: make the article more authoritative, explicit, structured, current, and independently useful—not just stuff it with keywords. Scrunch, for example, specifically exposes the URLs and domains shaping AI answers and lets you examine citation consistency. Scrunch

One important caveat

There isn't software that can guarantee an AI will cite your article. The models control their answer construction and citation behavior. The useful software is therefore observability + optimization, not a magic “make ChatGPT cite me” button.

For a publisher, I'd measure these KPIs separately:

• Citation rate: % of tracked relevant AI answers citing one of your URLs
• Citation share: your citations vs. competing publishers
• Article-level citation rate
• Citation position/prominence
• AI crawler visits
• AI → site referral sessions
• Brand/article mentions without links
• Which external sources AI trusts when it doesn't cite you

Profound explicitly distinguishes citations from cases where AI mentions a brand without providing a citation, while Scrunch similarly separates citations, AI-agent traffic and AI-referred traffic. Profound Knowledge Base Scrunch

If you tell me roughly what kind of publisher you are (news, magazine, niche information, financial, local, etc.) and your approximate article count/traffic, I can narrow this to 2–3 tools and tell you what I'd implement in the first 90 days.

There isn't software that can guarantee an AI will cite your article. The models control their answer construction and citation behavior. The useful software is therefore observability + optimization, not a magic “make ChatGPT cite me” button.

For a publisher, I'd measure these KPIs separately:

Yes. The category you’re looking for is usually called AI search visibility / AEO (Answer Engine Optimization) / GEO (Generative Engine Optimization). The important distinction is that you want tools that track actual citations of individual URLs, not merely whether your publication or brand is mentioned.

The tools I’d look at

• bing.com — start here, free. Microsoft now has an AI Performance report showing which of your URLs are cited in Copilot and Bing AI-generated answers, the grounding queries associated with those citations, and citation trends over time. This is particularly valuable for a publisher because it operates at the page/URL level, not just brand level. Bing Blogs Search - Microsoft Bing
• tryprofound.com — strongest enterprise-oriented option. It tracks which sources ChatGPT, Google AI Overviews, Perplexity and other answer engines cite, lets you compare your articles with competitors, and categorizes cited sources. It also turns citation data into content opportunities—e.g., identifying topics where competitors' pages are being cited instead of yours. Profound Profound
• scrunch.com — particularly interesting for publishers/content teams. Its citation tooling lets you see the actual URLs cited for tracked prompts, which prompts cite each URL, citation trends, competitors being cited, and which third-party sources are influential. Scrunch
• semrush.com / ahrefs.com — useful if you already have an SEO stack. Their newer AI-search capabilities are worth evaluating if you want to combine conventional search performance with AI visibility rather than buying an entirely separate AEO platform. Independent 2026 comparisons include both among the major AI-search monitoring options. TechnologyAdvice

But monitoring isn't the whole solution

For a publisher, I'd think about this as three separate problems:

1. Get cited — make articles easy for answer engines to retrieve and quote accurately.
2. Get attributed — make the publication, author, article title, and canonical URL unambiguous.
3. Measure the result — continuously test whether AI answers actually cite your URLs and whether you're losing citations to competitors.

The second point is especially important. There's no software switch that guarantees ChatGPT or Google will attribute an article. But you can make the source much easier to identify: strong author/entity information, explicit claims supported by evidence, descriptive headings, clean page structure, canonical URLs, dates/updates, original data, and clearly attributable facts. Microsoft's own guidance specifically recommends clear structure, evidence-backed claims, freshness, and consistency across formats for inclusion in AI answers. Search - Microsoft Bing

There's also a useful control mechanism: Bing supports data-nosnippet, which lets publishers prevent selected portions of a page from appearing in search/AI previews while keeping the page discoverable. That could be useful for paywalled or premium material. Bing Blogs

What I'd buy for a publisher

If you're a small/mid-size publisher, I'd start with:

Bing Webmaster Tools + Scrunch

That gives you first-party citation data from Microsoft's ecosystem plus broader prompt/citation monitoring.

If you're a large publisher with hundreds/thousands of articles and a dedicated SEO/editorial team, I'd demo:

Profound + Scrunch, and compare their URL-level citation coverage, prompt volume, API/export capabilities, and workflow integrations.

The key purchasing question I'd ask every vendor is:

"Can you show me exactly which of our article URLs were cited in AI answers, for which queries, alongside the competing URLs that were cited instead?"

If they can't answer that at URL level, you're probably buying a brand-mention dashboard, not a publisher-focused attribution system.

One other interesting finding: Scrunch's current analysis says that 87.2% of citation events it analyzed came from third-party sources rather than the brand's own site. If that pattern holds for publishers, it means your strategy shouldn't be limited to optimizing your own articles—you also need to understand which other sites, databases, journalists, and references AI systems use to establish authority around your subjects. Scrunch

If you tell me roughly how big the publication is (e.g. 100, 10,000, or 1M articles) and whether your priority is Google AI Overviews, ChatGPT, Perplexity, or all of them, I can narrow this down to the 2–3 tools I'd actually pilot.

Yes. The category you want is usually called AI search visibility, AEO (answer-engine optimization), or GEO (generative-engine optimization). For a publisher, I’d prioritize software that does both citation monitoring and editorial recommendations—not just a dashboard showing that traffic disappeared.

Strong options for a publisher

What I'd actually implement

For a publisher worried about losing traffic and losing attribution, I'd build the stack around four measurements:

1. Citation rate: For your important topics, how often does ChatGPT/Perplexity/Google AI actually cite one of your URLs?
2. Citation share: When AI answers a question in your category, are you being cited or is a competitor?
3. Article-level attribution: Which individual stories/pages are getting cited, and which aren't?
4. AI referral traffic: When an AI system does provide a link, does it result in a measurable visit?

The important distinction is that traditional SEO rank isn't enough anymore. A page can rank #1 in Google and still be absent from the generated answer. Current AEO tools increasingly expose the actual URLs AI systems use as sources. help.otterly.ai

One particularly interesting opportunity for publishers

Don't just optimize articles for being mentioned. Optimize them to be the source that gets linked.

That means testing things like:

• strong, explicit answers near the top of articles
• clearly attributed facts and primary sources
• descriptive headings that correspond to actual questions
• concise definitions and factual summaries
• structured data/schema where appropriate
• clear author and publication information
• dates and update timestamps
• unique reporting/data that AI cannot easily reproduce from 20 other sites
• internally consistent entities, terminology and facts
• pages that are easily crawlable and don't hide critical information behind scripts/paywalls

The software can tell you which of those characteristics correlate with citations in your vertical, rather than relying on generic "write for AI" advice.

If I were evaluating vendors for a real newsroom, I'd start with OtterlyAI and Goodie, then compare them against your existing SEO platform. Goodie is particularly interesting because it is explicitly built around the publisher/media use case, while OtterlyAI appears more mature as a general-purpose monitoring and measurement system. otterly.aihigoodie.com

One caveat: no software can guarantee that an AI will cite an article. The models' retrieval and ranking behavior isn't fully controllable. The goal is to measure citation probability, identify what sources/models prefer, and systematically improve the pages that are being skipped.

If you tell me roughly what kind of publisher you are (news, magazine, B2B, local, academic, etc.) and your monthly traffic/page count, I can narrow this to 3–4 tools and compare pricing, AI engines covered, article-level optimization, attribution analytics, and enterprise/API capabilities.

For a publisher, the goal is shifting from only ranking in search to also becoming a source that AI answer engines cite. The emerging category is usually called GEO (Generative Engine Optimization) or AI visibility optimization. getvisibly.appen.wikipedia.org

The software landscape is still young, but these are the main categories and tools worth evaluating:

A publisher-specific stack I would consider:

1. Measurement layer
   • Bing Webmaster Tools AI Performance (free)
   • One paid AI visibility tracker (Profound, Peec AI, Otterly, etc.)
   • Your analytics platform to track AI referral traffic

2. Editorial optimization layer
   • Audit articles that should be cited but are not.
   • Add:
     • clear author and publication dates
     • original reporting and data
     • explicit sourcing
     • concise “answer paragraphs” near the top
     • structured headings and FAQs
     • strong entity identification (who wrote it, who was interviewed, what organization is responsible)

These are common recommendations in GEO research and practice because AI systems need retrievable, attributable passages rather than just broadly relevant pages. www.shadow.inc

3. Create “citation magnets”
   • Original datasets
   • Rankings with methodology
   • Industry benchmarks
   • Investigations with unique facts
   • Expert interviews

AI systems have a strong incentive to cite sources that provide information unavailable elsewhere.

A few cautions:

• No tool can guarantee that ChatGPT, Gemini, Perplexity, or AI Overviews will cite an article. Citation behavior is partly controlled by the AI provider and changes over time. arxiv.org
• Be wary of vendors promising a direct “AI ranking score” equivalent to Google rankings; the field is still developing and measurement standards are unsettled. arxiv.org

For a news publisher, I would prioritize:

1. AI citation monitoring,
2. technical crawl/index health,
3. newsroom content audits,
4. original-reporting strategy.

The biggest strategic shift is: don’t just ask “how do we keep clicks?” Ask “how do we become the source an AI cannot answer without naming?”

Yes. The category you want is usually called GEO (Generative Engine Optimization) or AEO (Answer Engine Optimization). For a publisher, I’d look for software that does three separate jobs: (1) measure which articles AI systems cite, (2) identify why some articles get cited and others don't, and (3) connect AI visibility to actual traffic/revenue.

A few worth evaluating:

The last one is particularly interesting for a publisher: Microsoft's AI Performance dashboard reports total citations, cited URLs, grounding queries and citation trends. blogs.bing.comopenattribution.org

The broader market is still immature. Current comparisons distinguish between citation monitoring, content optimization, and actual attribution/revenue measurement—and most products are substantially better at the first than the third. www.petralabs.com

What I'd actually build into a publisher's stack

I'd prioritize:

1. Citation monitoring
Track a representative set of questions your readers ask and record:

• Was one of our articles cited?
• Which URL?
• Which passage/fact was used?
• Which AI engine?
• Which competitors were cited instead?
• Did the answer actually link to us?

2. Article-level "citability" analysis
The software should tell editors why an article isn't being selected. Research this year suggests that clear definitions, structured factual information, comparisons, original data/methodology and strong author attribution can correlate with greater citation likelihood. generative.qa

That suggests a useful editorial workflow: don't just optimize headlines for Google; make individual claims easy for an answer engine to retrieve, understand and attribute.

3. AI crawler/indexation monitoring
Make sure important articles are actually accessible to the relevant crawlers and aren't inadvertently blocked by robots.txt or other technical controls. This is a prerequisite rather than an optimization trick.

4. First-party measurement
Combine the GEO platform with your server logs/analytics. AI visibility dashboards can tell you that you're being cited, but they generally can't tell you all of the times your content influenced an answer without a click. That measurement problem is still unresolved industry-wide. openattribution.org

One important distinction

If your concern is specifically "AI is taking our traffic, how do we get credit when it uses our journalism?", I would not buy a tool solely because it gives you a "GEO score."

I'd ask vendors to demonstrate these five things on your actual articles:

1. URL-level citation detection
2. Passage/claim-level attribution
3. Coverage across ChatGPT, Google AI, Gemini, Perplexity and Copilot
4. Historical tracking so you can see citation gains/losses
5. Connection between AI citations/referrals and downstream subscriptions, registrations or revenue

That last requirement is important because the industry currently has a measurement gap between "AI cited us" and "AI exposure generated business value." www.petralabs.com

If you tell me roughly what kind of publisher you are (news, magazine, B2B, academic, local, etc.) and your monthly traffic, I can narrow this to 3–4 tools I'd actually put through an RFP, including approximate pricing and what each can/can't measure.

Yes. For a publisher, I’d think about this as “AI citation optimization” rather than simply SEO for LLMs. The goal is to make your articles easy for answer engines to discover, understand, select, and cite—with the citation pointing back to the original article.

The software I’d look at

If I were running a publishing company

I'd probably start with Profound + Cloudflare.

Profound answers:

Are AI systems actually citing our journalism, which stories are they citing, and what can we change to increase our citation share?

Its citation tooling can show the specific URLs AI systems use and compare your citation performance with competitors. www.tryprofound.comscrunch.comdevelopers.cloudflare.comopenattribution.orgdevelopers.cloudflare.comhelpcenter.scrunchai.comwww.tryprofound.com

Cloudflare answers:

Which AI systems are crawling our site, what are they doing with the content, and which types of AI access do we want to permit?

That's particularly useful because search/retrieval, AI training, and AI-agent access aren't necessarily the same thing. Cloudflare now distinguishes these categories and lets publishers make different policies. developers.cloudflare.com

Scrunch is particularly interesting if your problem is technical. For example, if your articles are buried behind JavaScript, poorly structured HTML, or complicated navigation, Scrunch's AXP is designed to give AI agents a cleaner, structured representation while leaving the normal site untouched. scrunch.comhelpcenter.scrunchai.com

The important caveat

There isn't software that can force ChatGPT, Gemini, Perplexity, etc. to cite you. These systems decide which sources to use based on factors such as relevance, authority, freshness, accessibility and the particular question.

So I'd use these products to create a feedback loop:

article → crawlability → AI retrieval → citation monitoring → identify gaps → improve article → measure citation change

And for a publisher, I'd measure citation share at the individual-article level, not just whether the publication's name appears somewhere in an AI answer. Profound, for example, now distinguishes citation share from broader visibility metrics. www.tryprofound.com

If you tell me what kind of publisher you are (news, magazine, B2B, reviews/affiliate, academic, etc.) and roughly how many articles you have, I can narrow this to the 3–5 tools I'd actually shortlist and what each would cost/require operationally.

Yes. The category you want is usually called GEO (Generative Engine Optimization), AEO (Answer Engine Optimization), or AI-search visibility/citation tracking.

The important distinction is that no software can guarantee attribution. What the better tools can do is (1) measure whether AI systems are citing your URLs, (2) identify the queries/topics where you're being cited or overlooked, and (3) help editors make content easier for answer engines to retrieve and attribute.

The tools I'd look at

If I were running a publisher

I'd build the stack in three layers:

1. Measure citations first.
Start with Bing Webmaster Tools AI Performance. It's unusually valuable because it reports actual citation activity, rather than giving you an inferred "AI score." It can show the individual pages being cited and the queries associated with them. www.bing.com

2. Add cross-engine monitoring.
Use a dedicated GEO platform such as Scrunch, Peec AI, or Otterly if you need to monitor ChatGPT, Perplexity, Gemini, Google AI experiences, etc. This gives the editorial team a much broader picture than Bing alone. www.techradar.comdistribution.studio

3. Feed the findings back into your CMS/editorial process.
The goal isn't to "write for AI." It's to make your journalism easy to identify, extract, verify, and attribute:

• Put the answer to the article's central question clearly near the top.
• Use descriptive H2/H3s that correspond to real questions.
• Make factual claims explicit rather than burying them in prose.
• Include original data, reporting, quotes, methodology, and primary sources.
• Clearly identify authors, publication dates, updates, and the publisher.
• Keep important facts in HTML rather than only in images/graphics.
• Maintain strong canonical URLs and clean internal linking.
• Update important articles and make the update date unambiguous.

Bing itself specifically recommends clear structure, tables/FAQ-style sections, evidence-backed claims, freshness, and consistent representation across formats for inclusion in AI answers. www.bing.comwww.bing.com

One thing I'd not buy into

Be skeptical of vendors promising that adding special "AI optimization" markup or changing wording will force ChatGPT/Google/Perplexity to cite you. There isn't a magic attribution tag.

In fact, the current evidence points toward ordinary technical SEO + excellent, authoritative content + clear structure + measurement of actual citations rather than a secret AI-specific trick. Bing explicitly says the same underlying crawling/indexing/SEO fundamentals support eligibility for AI grounding and citations. www.bing.comwww.bing.com

And there's a bigger strategic opportunity here: optimize for being the source, not merely being mentioned. A publisher should track URL citations and citation share, not just whether its brand appears somewhere in an AI answer. Bing's newer AI Performance reporting is moving in exactly that direction. www.bing.com

If you tell me roughly what kind of publisher you are (news, magazine, niche information, reviews, B2B, etc.) and your CMS/SEO stack, I can narrow this to the 3–5 tools I'd actually evaluate, including approximate pricing and what each would give your editorial team.

Yes. The category you want is usually called AI search optimization / answer-engine optimization (AEO). For a publisher, I’d separate the software into (1) citation intelligence, (2) content optimization, and (3) crawler/traffic control.

My shortlist

The important distinction

If your goal is “make my article more likely to be cited, with the citation pointing back to my article,” I would not start by blocking AI crawlers.

Instead, I'd use something like Profound or Scrunch to answer:

1. Which of our articles are already being cited?
2. Which articles should be cited but aren't?
3. What competing articles are being cited instead?
4. Which specific URLs does ChatGPT/Perplexity/Gemini/Google AI Overviews use for this topic?
5. What characteristics do those pages have that ours doesn't?
6. When we change an article, does its citation rate actually improve?

Profound, for example, explicitly tracks the individual URLs being cited and lets you compare citation share by topic, prompt and platform. www.tryprofound.com Scrunch similarly records the complete URLs cited in monitored AI responses and lets you examine them at the page level. helpcenter.scrunchai.com

For a publisher, I'd build a stack like this

CMS → article optimization → AI visibility monitoring → crawler/traffic analytics

For example:

Profound + Cloudflare

Profound tells your editorial/SEO team “these 200 articles are being cited, these 500 aren't, and here is what competitors are doing.” Its current product also evaluates things such as readability, freshness, content structure, information density, machine readability and answerability. www.tryprofound.comwww.tryprofound.com

Cloudflare tells your infrastructure team “these AI crawlers are accessing us this much, and these ones are actually sending humans back.” It can distinguish AI crawlers/assistants and provide allow/block controls. developers.cloudflare.com

That combination gets you much closer to answering the business question you're really asking: “Are AI systems taking our journalism without sending anything back, or are they discovering, citing and referring readers to us?”

One particularly interesting development is that Cloudflare is moving toward AI attribution/traffic analytics and content monetization, rather than simply treating AI crawlers as unwanted bots. www.cloudflare.com

One caveat

There isn't currently a magic “add this markup and ChatGPT will cite us” switch. Citation behavior is controlled by the individual answer engine. The practical strategy is therefore to make your articles easy to retrieve, understand and attribute, then measure citation performance empirically.

For a publisher, I'd prioritize:

• clear authorship and publication/update dates
• strong first-party sourcing and original reporting
• explicit claims followed by supporting evidence
• clean HTML and headings
• highly specific answers to identifiable questions
• structured data where appropriate
• stable, canonical article URLs
• avoiding important content hidden behind JavaScript
• making the original source unmistakable
• monitoring whether AI answers actually link to the original article, rather than merely mentioning the publisher

If you tell me roughly how big the publisher is (e.g. 500, 50,000, or 5 million articles), your CMS, and whether you're more concerned about ChatGPT/Perplexity or Google AI Overviews, I can narrow this down to 2–3 tools and a practical implementation architecture.

Yes. The category you want is increasingly called AI search optimization / Answer Engine Optimization (AEO). For a publisher, I’d separate the problem into (1) measuring whether your journalism gets cited, (2) figuring out why it gets cited or ignored, and (3) controlling AI crawler access.

Tools I’d look at

For your particular concern, I'd prioritize Profound or Scrunch

The key distinction is "AI visibility" vs. "AI citation."

You don't merely want an AI to mention your publication. You want something like:

According to [Your Publication], … [link to your article]

And ideally the citation is attached to the specific article that actually contains the reporting.

Profound is particularly interesting if your goal is to build a publisher-wide citation strategy. It can show which URLs are cited, which competitors are taking citations from you, and which third-party sites are influential sources. That can turn into editorial questions such as "Why is another publication getting cited for this topic when we have the original reporting?" www.tryprofound.com

Scrunch is attractive if you also want to understand the traffic/referral side. Its platform tracks citations alongside AI referrals and crawler traffic. scrunch.comscrunch.com

The other half is infrastructure

I'd strongly consider Cloudflare AI Crawl Control even if you use Profound/Scrunch for measurement.

It gives you visibility into crawlers such as OpenAI's OAI-SearchBot, Anthropic's Claude-SearchBot, and Perplexity's PerplexityBot, and lets you decide which ones can access your content. developers.cloudflare.com

That's important because "let AI search engines discover and cite my articles" and "let AI companies freely ingest my articles for model training" are not necessarily the same business decision.

Cloudflare is also adding analytics specifically around AI consumption and human referrals, which is getting closer to the publisher question of "Is this AI access actually sending us anything back?" blog.cloudflare.com

What I'd actually build for a publisher

I'd use a stack roughly like:

Profound/Scrunch → identify citation opportunities → editorial/content changes → Cloudflare → control/measure crawler access → analytics → measure AI referrals

And I'd establish a dashboard with metrics such as:

• Citation rate: % of relevant AI answers citing your publication
• Article citation rate: which individual stories get cited
• Citation share vs. competitors
• Citation position/prominence
• AI referral traffic
• AI crawler volume
• AI crawler → human referral ratio
• Topics where you have original reporting but competitors get the citation
• Stories that are highly cited but generate little/no referral traffic

One important caveat: there isn't a software switch that guarantees attribution. The tools can measure and optimize the factors associated with being cited, but the answer engine ultimately decides what sources to retrieve and cite. And you should be wary of vendors promising guaranteed placement; for example, Perplexity has recently pushed back on attempts to manipulate AI results through bot-specific advertising/cloaking. www.businessinsider.com

If you tell me roughly what kind of publisher you are (news, magazine, trade/B2B, recipe, financial, local, etc.) and your CMS/CDN (WordPress, Drupal, Arc, WordPress + Cloudflare, etc.), I can narrow this to a 3–5 tool shortlist and a practical implementation plan, including what I'd measure before spending money.

Yes. The important distinction is that no software can force an AI answer to cite you, but there are now tools that can improve your odds, measure whether you're being cited, and control which AI crawlers get access.

For a publisher, I'd look at these categories:

If attribution is the specific goal

I'd prioritize NewzDash + Cloudflare rather than buying a generic "AI SEO" product.

1. Establish a citation baseline.
Track your important stories/queries and record:

• Is an AI answer appearing?
• Is your publication cited?
• Is the citation a link or merely a brand mention?
• Is the citation pointing to the original article or a syndicated/aggregated copy?
• How prominent is your citation?
• Which competitors are being cited instead?

That's essentially the framework NewzDash recommends for publishers. www.newzdash.com

2. Make the underlying articles easy to retrieve and attribute.
This means strong canonical URLs, clear authorship/bylines, publication and update dates, structured data, descriptive headings, original reporting/data, and clean internal linking. An llms.txt layer can additionally provide AI systems with a machine-readable map of your content, although I would treat it as complementary rather than a magic ranking signal. developers.cloudflare.com

3. Don't blindly block AI crawlers.
There's a strategic difference between a crawler that trains a model and one that retrieves your article for an answer that links back to you. Cloudflare explicitly lets publishers distinguish and manage crawlers individually; its documentation recommends allowing crawlers that provide citations/referrals while blocking others that don't fit the publisher's strategy. developers.cloudflare.com

4. Measure the actual payoff.
AI visibility and traffic aren't the same thing. NewzDash notes that a publisher can gain substantial citation visibility while receiving little referral traffic because the AI answer satisfies the user without a click. www.newzdash.com

The stack I'd recommend for a serious publisher

CMS → technical SEO/schema → llms.txt → Cloudflare AI Crawl Control → AI citation monitoring (NewzDash) → analytics/dashboard

And I'd give the editorial team a KPI such as:

% of priority queries where our original article is cited with a clickable link

rather than the much less useful metric "how often does AI mention our brand?"

One other reason I'd take this seriously: this isn't just hypothetical traffic anxiety. A French press association recently cited estimates of 33–38% traffic declines to media sites associated with AI summaries, illustrating why publishers are increasingly treating AI citation and referral as a distinct distribution channel. www.reuters.com

If you tell me roughly what kind of publisher you are (news, magazine, specialist B2B, local, etc.) and your CMS, I can narrow this down to the 3–5 tools I'd actually buy, including approximate pricing and what I'd implement first.

Yes. But I’d separate “getting cited” from “getting proper attribution.” There is no software that can force ChatGPT, Google, Perplexity, etc. to cite you correctly. The useful stack is one that measures citations, identifies what makes your pages citable, improves the content, and controls AI crawler access.

The software I’d look at

What I'd actually recommend for a publisher

I wouldn't start by buying an expensive “AEO writing” tool. I'd build a citation optimization loop:

1. Measure → Bing Webmaster Tools + Ahrefs/Semrush

Find out:

• Which articles are already being cited
• Which articles competitors are getting cited for
• What questions/queries trigger citations
• Whether AI systems cite the article itself or merely mention the publication
• Which specific passages get represented accurately vs. paraphrased incorrectly

Bing's AI Performance report is especially interesting because it now exposes page-level citation activity and grounding queries, rather than giving you a vague “AI visibility score.” www.bing.com

2. Make articles easier for AI systems to quote accurately

This is more important than adding some magical llms.txt file.

For important factual articles, I'd have your CMS/editorial workflow enforce:

• A concise answer immediately below important question headings
• Explicit claims rather than vague prose
• Named authors and subject-matter credentials
• Publication and last-updated dates
• Original statistics and clearly labeled methodology
• Links to primary sources
• Quotable definitions and explanations
• Clear tables/lists where appropriate
• Consistent entity names
• Strong internal linking
• Appropriate structured data

Google's own guidance is notably conservative: it says there are no special technical requirements or special AI optimizations for appearing in AI Overviews/AI Mode; normal technical SEO and helpful, reliable, people-first content remain the foundation. developers.google.com

3. Treat attribution as a product requirement

This is the part I think publishers should be particularly aggressive about.

For example, instead of an article saying:

Experts expect the market to grow rapidly.

make the underlying page contain something more like:

The market is projected to grow 18% annually through 2030, according to [named research organization], based on [methodology].

That gives an AI system a much cleaner factual unit to retrieve and attribute.

4. Use Cloudflare for the rights side

This is complementary to citation optimization. Cloudflare can tell you which AI crawlers are actually consuming your content and lets you establish different policies for them. developers.cloudflare.com

That's potentially important for a publisher deciding:

“We'll permit crawlers that reliably send attribution/referral traffic, but require licensing/payment or block crawlers that extract our journalism without meaningful attribution.”

Cloudflare's current tooling even exposes pay-per-crawl as an emerging monetization mechanism. developers.cloudflare.com

My shortlist

If I were evaluating this for a mid-size or large publisher, I'd probably test:

Best low-cost starting point:
Bing Webmaster Tools + Google Search Console + Cloudflare

Best SEO-team stack:
Ahrefs Brand Radar + Cloudflare

Best if you're already a Semrush shop:
Semrush AI Visibility Toolkit + Cloudflare

Best enterprise/publisher investigation:
Scrunch + Authoritas + Cloudflare

One important caveat: citation frequency isn't the same thing as traffic or revenue. Bing explicitly warns that a citation means your content was referenced; it does not mean the user clicked through. www.bing.com That's why I'd instrument AI citations separately from referrals and ultimately optimize for “cited + accurately represented + attributable + click-worthy.”

If you tell me roughly what kind of publisher you are (news, magazine, B2B, recipes, reviews, local, etc.) and your approximate article count, I can narrow this down to a practical $0 / <$1k-month / enterprise software stack.

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

For a publisher, I’d separate the problem into three jobs: (1) determine whether AI systems cite you, (2) figure out why they cite competitors instead, and (3) make your articles easier for AI systems to understand and attribute correctly.

My shortlist

If you're a serious publisher, I'd look hardest at Profound and Scrunch. The distinction is roughly:

• Profound: "What is AI saying, what is it citing, and how do we increase our share?"
• Scrunch: "What is AI doing on our site, which pages does it consume, and how can we technically make those pages more AI-readable?"

The important caveat

There isn't a magic piece of software that can guarantee ChatGPT, Google AI Overviews, Perplexity, Gemini, etc. will cite an article. The systems choose sources dynamically, and research suggests AI search has different source-selection behavior from conventional Google ranking. arxiv.org

For a publisher, I'd therefore optimize for citation probability and attribution quality, not simply "AI visibility."

That means your workflow should look something like:

Article → structured/content audit → AI prompt testing → citation monitoring → identify missing/competing sources → revise article → re-test → measure AI referrals

A particularly useful metric is citation rate at the URL level: When an AI answers a question that our article could answer, how often does it actually cite our article? That's much more actionable than simply counting brand mentions.

Don't overlook the technical layer

You also want analytics/control over AI crawlers. Cloudflare, for example, has AI Crawl Control, which lets publishers see and control how AI crawlers access their content and has mechanisms aimed at publishers interested in licensing rather than simply allowing unrestricted crawling. blog.cloudflare.com

So I wouldn't necessarily buy one giant "AI SEO" package and call it done. A strong publisher stack could be:

Profound or Scrunch + your existing SEO/content stack + GA4/log/CDN data + editorial changes.

If you tell me roughly how big the publisher is (e.g. 100 articles vs. 100,000), your CMS, and whether your priority is traffic, citations/brand exposure, or licensing AI access, I can narrow this to 2–3 tools and explain what I'd actually buy.

Yes. The category you want is usually called GEO/AEO (Generative Engine Optimization / Answer Engine Optimization), but for a publisher I’d think about it as three separate problems:

1. Get AI systems to discover and cite your articles
2. Measure which articles actually get cited
3. Control/monetize AI crawling so citation doesn't simply become free traffic replacement

There are now several useful software options.

My shortlist for a publisher

The important distinction

I wouldn't buy one of these simply because it promises "more AI citations." The evidence around GEO is still immature. Recent research finds that topical relevance and retrieval position are important, while generic "AI optimization" tricks aren't reliably transferable across systems. arxiv.orgarxiv.org

For a publisher, I'd instead build a measurement loop:

Article → AI crawler discovers it → AI retrieves it → AI cites it → citation links back → reader visits → subscription/ad/revenue

The software should tell you where that chain breaks.

For example, Ahrefs explicitly distinguishes "cited" from "found but not cited." That's an extremely useful metric for a publisher: if 10,000 articles are being retrieved but only 500 are actually cited, you've got a very different optimization problem than if they're not being retrieved at all. help.ahrefs.comwww.semrush.comdevelopers.cloudflare.comhelp.ahrefs.comwww.semrush.com

What I'd prioritize technically

For your articles, I'd optimize for things that make the source easy to retrieve, understand and attribute, rather than trying to "game" an LLM:

• Put the answer/finding near the top of the article.
• Make claims specific and attach them to evidence.
• Clearly identify the author, publication date, update date and original reporting.
• Use descriptive headings that correspond to real questions.
• Make important facts independently extractable rather than burying them in long prose.
• Maintain strong internal linking and canonical URLs.
• Mark up relevant entities/data with appropriate structured data.
• Make the distinction between your original reporting and material sourced elsewhere explicit.
• Track individual article URLs—not just your publication's overall brand visibility.

That's consistent with the emerging research: relevance, completeness and trust cues help, but formatting tricks alone aren't a magic bullet. arxiv.orgarxiv.org

There's also a bigger issue: attribution ≠ traffic

This is probably the most important point for your publisher.

You can succeed at getting cited and still lose traffic. Semrush describes exactly this phenomenon: its content was being cited hundreds of times while blog traffic was falling. www.semrush.com And a recent study of Google AI Overviews found that clicks to cited sources were rare, around 1% of AI Overview visits in its sample. arxiv.org

So I'd measure at least these four KPIs:

AI retrieval rate → citation rate → citation prominence → downstream referral traffic

rather than simply "AI mentions."

One thing publishers should do now

Also look at Google Search Console's generative-AI controls/reporting. Google is rolling out controls that let site owners manage whether their content is included in AI Overviews, AI Mode and generative-AI Discover features. support.google.com

And Cloudflare is particularly interesting if you're worried about the economic asymmetry: "AI gets our content, but we get no meaningful audience in return." Its AI Crawl Control gives publishers visibility into which AI crawlers are accessing content and lets them impose crawler-specific policies. developers.cloudflare.com

If I were advising a mid-to-large publisher today, my starting stack would be Ahrefs Brand Radar + Google Search Console + Cloudflare AI Crawl Control. Add Profound or Adobe LLM Optimizer if you need enterprise-scale monitoring and workflow automation.

If you tell me roughly how large the publisher is (e.g. 100, 10,000, or 1M+ articles) and whether you're primarily news, magazine, reference, or niche editorial, I can narrow this down to a 2–3 tool stack and explain exactly what I'd measure.

Yes. For a publisher, I’d think about this as AI citation optimization, rather than simply “AI SEO.” The goal is not just to get your publication mentioned—it’s to increase the odds that AI systems retrieve the article, cite the specific URL, and send the reader back to you.

The software market is still young and fragmented, so I’d prioritize tools that distinguish “AI found/read this page” from “AI actually cited this page.” That distinction matters a lot: research has found a substantial gap between pages retrieved by AI systems and pages that ultimately receive attribution. arxiv.org

My shortlist

For a publisher, I'd use them slightly differently

1. First, establish a citation baseline.
Take your major editorial topics and measure:

• Which articles are being cited?
• Which are being retrieved but not cited?
• Which competitors are cited instead?
• Which AI questions trigger citations to your publication?
• Which articles get cited by one engine but ignored by another?
• Are AI answers linking to the article itself, the homepage, or some other page?

Ahrefs is particularly interesting here because it explicitly distinguishes “cited,” “found but not cited,” and “not found.” docs.ahrefs.com

2. Then optimize the articles themselves.
The software should help you discover patterns such as:

“AI frequently retrieves our investigative pieces but cites Reuters instead.”

or:

“Our explanatory articles are cited when they contain original statistics, named sources, dates, and concise answers.”

That's much more actionable than an abstract “AI visibility score.”

3. Optimize your external authority, too.
This is an important difference from traditional SEO. AI systems don't necessarily cite the publisher with the best article; they may assemble an answer from multiple sources. Peec's research, for example, emphasizes that external sources can strongly influence which brands/content appear in AI answers. peec.aipeec.ai

So I'd track who is citing you, who is citing your competitors, and which third-party sites influence AI answers in your subject areas.

One caveat I'd take seriously

I wouldn't buy any product promising that it can guarantee attribution. AI answers are nondeterministic, and the same question can produce different sources at different times. Ahrefs itself describes AI visibility as something that needs to be measured probabilistically rather than like a traditional Google ranking. ahrefs.com

Also, citation rate isn't the same thing as traffic recovery. You should pair the AI-visibility platform with your normal analytics and measure actual referrals, engagement, subscriptions/conversions, and branded search demand. Semrush explicitly recommends combining AI citation/mention monitoring with web analytics because AI doesn't consistently expose click-level referral information. www.semrush.com

If I were evaluating this for a mid-to-large publisher, I'd demo Scrunch + Ahrefs Brand Radar first, and Peec as the citation-intelligence comparison. The key question I'd put to all three is:

“Show me the articles on our domain that were retrieved by AI but not cited, and tell me what characteristics distinguish them from our articles that do get cited.”

That's much closer to the business problem you're actually trying to solve than generic “GEO score” reporting.

Yes. For a publisher, I’d think about this as two separate software problems:

1. Make your pages easier for AI systems to discover, understand, and cite.
2. Measure which articles actually get retrieved/cited in AI answers, rather than relying on traditional Google rankings.

The second category is much more mature right now.

My shortlist

The one I'd start with: Ahrefs

For a publisher, the distinction between "AI found my article" and "AI actually cited my article" is extremely important.

Ahrefs explicitly tracks both:

• Cited — AI retrieved your page and referenced it in the answer.
• Found but not cited — AI retrieved your page as a potential source but ultimately didn't cite it.
• Not found — AI didn't retrieve your page. help.ahrefs.com

That gives you a very useful editorial funnel:

published article → retrieved by AI → cited by AI → citation generates referral traffic

You can then ask, for example:

"Of our 50,000 articles, which topics/pages are frequently retrieved by ChatGPT and Perplexity but rarely cited?"

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

Ahrefs also lets you analyze specific URLs and site sections, which is particularly useful for a large publisher with thousands or millions of articles. ahrefs.com

But software alone won't solve attribution

There's an important caveat: you can't reliably force an AI answer to cite you.

Research into LLM search has found a substantial gap between pages systems retrieve and pages they actually cite. arxiv.org

So I'd invest in a combination of technical AI accessibility + editorial optimization + measurement.

For example:

Technical layer

• Make important article text accessible without JS-dependent rendering.
• Use clean canonical URLs.
• Keep author/date/update information explicit.
• Use structured data appropriately.
• Make pages fast and easily crawlable.
• Carefully manage AI crawler permissions rather than blindly blocking them.

Editorial layer

• Put the answer to the question clearly near the top.
• Use descriptive headings and concise factual passages.
• Include original reporting, data, quotations, methodology and primary-source links.
• Make claims attributable and verifiable.
• Keep important facts on stable, canonical URLs.
• Update high-value evergreen pieces rather than continually creating near-duplicates.

Measurement layer

• Track retrieval → citation → referral.
• Track individual URLs, not just your domain.
• Monitor which competitors are being cited instead.
• Track citation rates by topic, author, article type and publication date.
• Compare AI referral traffic with the much larger volume of zero-click AI exposure.

That last point matters: Semrush itself notes that being cited can increase exposure while traffic to the underlying article still falls. www.semrush.com

If you're a serious publisher, I'd build the stack this way

Ahrefs Brand Radar → understand which articles AI systems use and cite

Your analytics/CDP → understand whether those citations produce visits, registrations, subscriptions, etc.

Scrunch or equivalent technical tooling → understand how AI crawlers interact with your site and whether your pages are machine-readable

Editorial CMS/workflow → turn the findings into changes to article structure, internal linking, metadata and original reporting.

The strategic goal shouldn't simply be "get cited more." I'd optimize for:

Get the right article cited, with the publisher name and article link intact, on high-value questions, while preserving as much downstream traffic/conversion as possible.

If you tell me roughly what kind of publisher you are (news, magazine, B2B, research, local, etc.) and your article volume, I can narrow this to a 2–3 tool stack and explain exactly what I'd measure.

Yes. The category you want is usually called AI visibility / Answer Engine Optimization (AEO) / Generative Engine Optimization (GEO) software. The important distinction is that no tool can guarantee attribution—AI systems choose sources dynamically—but several tools can help you measure citations, identify what gets cited, and improve the pages themselves.

My shortlist for a publisher

The one I'd look at first

For a publisher worried specifically about traffic loss, I'd investigate Scrunch first, rather than buying a generic SEO tool.

The reason is that your problem actually has three separate parts:

1. Are AI systems finding our articles?
2. Are they actually citing our articles when they answer questions?
3. Do those citations produce visits/revenue?

A good publisher stack needs to measure all three. Scrunch is interesting because it attempts to connect AI visibility and crawler activity with referral/conversion data. www.techradar.com

But don't overlook the free/technical layer

I'd pair that with Cloudflare AI Crawl Control if your site is on Cloudflare. It can show which AI crawlers are hitting your pages, monitor robots.txt compliance, and let you make crawler-specific allow/block decisions. Cloudflare explicitly recommends allowing crawlers when they provide value through citations or referrals. developers.cloudflare.com

That's important because blocking all AI crawlers isn't necessarily the answer. If an AI search engine sends readers back to you, you want that crawler; if another system consumes your material without meaningful attribution or referral, you may want a different policy.

Cloudflare also now supports redirecting AI training crawlers from obsolete URLs to their canonical versions, which can help ensure that the version an AI system encounters is the one you actually want associated with your publication. developers.cloudflare.com

And there's an important caveat about "GEO"

I'd be skeptical of any vendor promising a magic formula for getting cited.

Google itself says there are no special technical requirements or additional optimizations specifically required for AI Overviews or AI Mode; its normal SEO fundamentals still apply. developers.google.com

The more promising approach is to make your journalism highly extractable and attributable:

• Put the direct answer near the top.
• Use descriptive H2/H3s framed around actual questions.
• Clearly identify dates, numbers, people, organizations and sources.
• Make original reporting and primary-source evidence explicit.
• Keep important facts in HTML rather than burying them in images/interactive elements.
• Use strong canonical URLs and clean internal linking.
• Give individual claims enough surrounding context that an AI can understand who said what.
• Monitor which competitors' pages are being cited for the same questions.

Recent research supports this general direction: highly influential AI-cited pages tend to be structured, semantically aligned, and rich in easily extractable evidence such as definitions, numerical facts, comparisons and procedures. arxiv.org

If I were designing the publisher's stack, I'd do:
Scrunch (AI visibility + attribution analytics) + Cloudflare AI Crawl Control (crawler governance) + your existing Google Search Console/analytics + editorial guidelines for AI-extractable reporting.

That gives you a much better answer to the executive question than simply "our SEO rankings are up/down": which stories are being consumed by AI, which ones are getting credited, by whom, and whether those citations send readers back.

If I were advising a publisher today, I'd think about this in four layers: measurement, optimization, access control/licensing, and attribution/monetization. There isn't yet a single product that guarantees AI systems will use and attribute your content, but there are several useful categories.

1. AI visibility software

This is the equivalent of SEO rank tracking, except for AI answers.

The strongest entrants include:

• urlScalePostturn0search2
  • Tracks actual AI fetches and citations from CDN logs rather than relying only on prompting models.
  • Useful for publishers selling sponsorships because it can demonstrate AI influence. www.scalepost.ai

• Profound
  • Popular with enterprise brands.
  • Measures share of voice across AI assistants.

• Scrunch AI
  • Focuses on "answer engine optimization" (AEO/GEO).

These tools answer questions like:

• Which articles get cited?
• Which competitors are cited instead?
• Which prompts produce citations?
• Which AI products send referrals?

2. AI content marketplaces

Instead of hoping models scrape you, these try to establish licensed relationships.

TollBit

Good if you're a mid-size or large publisher.

Features include:

• AI bot analytics
• licensing
• access controls
• structured AI endpoints
• monetization of AI access

It is positioning itself as infrastructure between publishers and AI companies. tollbit.com

ProRata

A different model.

Their focus is:

• licensed publisher network
• explicit attribution
• revenue sharing
• proportional credit when your content contributes to an answer

Their consumer search product is intended to demonstrate attribution-first AI search. prorata.ai

3. Technical optimization

This matters almost as much as software.

Many publishers are adding:

• structured data (Schema.org)
• clearly answerable headings
• FAQ sections
• author information
• cited statistics
• canonical URLs
• machine-readable licensing
• llms.txt (still experimental, with uneven adoption across AI systems) www.scalepost.aiwww.reddit.com

Think of it as making your content easy to retrieve, quote, and attribute.

4. Analytics

Traditional analytics miss a lot of AI activity.

Many publishers now combine:

• Google Analytics
• Search Console
• CDN logs
• AI crawler analytics
• AI citation tracking

to understand:

• AI retrievals
• AI referrals
• AI citations
• human click-through after AI answers

If I ran a publisher today

I'd probably implement this stack:

• Cloudflare for AI crawler controls and analytics.
• ScalePost to measure actual AI citations.
• TollBit to license and monetize AI access.
• ProRata to participate in an attribution-focused publisher ecosystem.
• Continue investing in traditional SEO while also optimizing for AI answer extraction (clear structure, strong sourcing, and comprehensive coverage).

The bigger strategic shift is that publishers are increasingly optimizing for being the cited source inside AI answers, not just ranking first in search results. Software can help you measure and monetize that, but the strongest predictor of citation is still producing authoritative, well-structured content that AI systems can confidently attribute.

If you're a publisher, there are really two separate goals:

1. Increase the chance that AI systems can accurately understand and cite your content.
2. Measure and optimize AI referral traffic and citations.

The software ecosystem is still developing, but there are already useful tools in each category.

Improving the odds of attribution

No software can guarantee that an AI assistant will cite or link to an article. Different AI products have different retrieval systems, policies, and licensing arrangements. However, publishers consistently benefit from making content easy to identify, understand, and verify.

The biggest improvements usually come from:

• Publishing with clear bylines, publication dates, and update dates.
• Using descriptive headings that answer specific questions.
• Adding structured data (Article, NewsArticle, FAQ where appropriate).
• Using stable canonical URLs.
• Making original reporting easy to distinguish from commentary.
• Including primary sources and citations.
• Avoiding fragmented content spread across many near-duplicate pages.

Emerging AI-specific optimization tools

A newer category often called AI Search Optimization (AISO) or Generative Engine Optimization (GEO) focuses specifically on AI assistants.

Some vendors include:

• Profound
• Peec AI
• Goodie AI

These platforms typically monitor:

• which AI products mention your publication,
• what prompts surface your content,
• competitor visibility,
• citation frequency,
• changes over time.

Think of them as analogous to SEO rank trackers, but for AI-generated answers.

If you run a news or editorial site

I'd prioritize investment in this order:

1. Strong technical SEO (crawlability, canonicals, sitemaps).
2. Comprehensive Schema.org markup.
3. Original reporting with explicit sourcing.
4. AI visibility monitoring (Profound or similar).
5. Analytics segmented for AI referrals.
6. Server log analysis to understand AI crawler behavior.

Looking ahead

The industry is moving toward richer attribution and publisher controls. Some AI systems already provide links or citations in many responses, and publishers are experimenting with standards that make attribution easier. While there isn't yet a universal mechanism that guarantees attribution across all AI products, improving content quality, technical structure, and discoverability gives your articles the best chance of being identified and credited when AI systems choose to reference external sources.

For a publisher, the goal is usually not to “game” AI answers, but to make your content easier for AI systems to understand, retrieve, cite, and attribute correctly. The tooling is still evolving, but these categories are useful:

For a serious publisher, I’d prioritize this stack:

1. Measurement layer: Profound or Scrunch
   • Track: “When someone asks an AI about our topics, are we appearing as a cited source?”
2. Technical layer: Cloudflare + strong crawl rules
   • Avoid accidentally blocking AI systems that provide referral traffic or citations. developers.cloudflare.com
3. Editorial layer: Improve “citation-worthiness”
   • Original reporting
   • Clear author attribution
   • Dates and updates
   • Tables, definitions, FAQs, and structured facts
   • Primary sources and references
   • Unique data that AI systems cannot easily summarize from elsewhere

A caveat: no tool can guarantee that ChatGPT, Gemini, Perplexity, or other systems will cite an article. These tools mostly help you measure visibility, diagnose problems, and improve the odds. AI answer attribution remains a moving target. www.wsj.com

If you are a news publisher, magazine, or niche content site, the best choice changes quite a bit depending on whether your priority is traffic recovery, licensing AI access, or getting cited as the authority.

There isn't a single piece of software that guarantees your articles will be used by AI systems with attribution. Today, attribution depends on how individual AI products retrieve, rank, and cite sources. However, there are tools that can materially improve your odds by making your content easier to discover, trust, and cite.

I'd group them into four categories:

For publishers, the highest-ROI work is usually editorial

Most AI answers cite sources that exhibit:

• Original reporting
• First-party data
• Exclusive interviews
• Clear authorship
• Publication dates
• Citations to primary sources
• Good internal linking
• Stable URLs
• Fast page loads

AI systems are generally much more likely to attribute a publication that is the original source than one that rewrites wire copy.

Add structured data everywhere

Software can automate:

• Article schema
• Author schema
• Organization schema
• FAQ schema (when appropriate)
• Image licensing metadata
• Citation metadata

This helps search engines and AI retrieval systems identify authoritative sources.

Publish an llms.txt file—but don't expect miracles

Several CMS plugins and services now generate llms.txt automatically. For example, Yoast recently added support, and there are dedicated WordPress plugins. The file provides a machine-readable map of your most important content. However, evidence so far suggests that adoption by major AI systems remains limited, so it should be viewed as a low-cost, future-facing enhancement rather than a proven ranking signal. developer.chrome.com

Build pages AI likes to cite

The content format matters as much as the software. Publishers seeing the most AI citations often create:

• Definitive explainers
• Statistics pages
• Timelines
• Glossaries
• Research reports
• Original datasets
• "Last updated" evergreen guides

These pages are frequently retrieved because they answer factual questions directly while providing a canonical source.

Monitor which AI systems are actually using you

One capability many publishers overlook is log analysis. Rather than guessing, track:

• GPTBot
• OAI-SearchBot
• ChatGPT-User
• PerplexityBot
• ClaudeBot
• Google-Extended

This reveals whether AI services are crawling your content and which sections of your site they access.

If I were advising a news publisher today

I would prioritize:

1. AI visibility monitoring (Profound or Peec AI).
2. Comprehensive schema markup (WordLift or Schema App).
3. Server log monitoring for AI crawlers.
4. Editorial workflows that emphasize exclusive reporting, original data, and clear sourcing.
5. An llms.txt file as a low-effort addition, while recognizing that its practical impact is not yet well established. developer.chrome.com

For most publishers, the biggest gains don't come from a single "AI SEO" tool—they come from combining strong technical foundations with content that is uniquely valuable enough for AI systems to cite rather than summarize from secondary sources.

Publishers are increasingly shifting from pure SEO to what some call AI discoverability, LLM optimization (LLMO), or Generative Engine Optimization (GEO). The goal isn't just to rank in search results, but to maximize the chances that AI systems retrieve, trust, and attribute your content.

There isn't a single "buy this and you'll get cited" product, but there is a growing stack of tools that help.

The most useful tools today

1. Profound
Probably the best-known platform focused specifically on AI search visibility.

It tracks:

• whether ChatGPT, Claude, Gemini, Perplexity, and others mention your publication
• which competitors get cited instead
• which prompts trigger your content
• citation frequency over time

This is one of the few products built specifically for publishers.

2. Peec AI

Useful if your goal is measuring:

• AI citations
• brand mentions
• answer share
• prompt tracking

Think of it as "Google Search Console for AI," although the ecosystem is still evolving.

3. Goodie AI

Another newer platform that monitors visibility inside AI-generated answers.

Many publishers use these kinds of products to identify articles that are already close to being cited and improve them rather than rewriting everything.

4. Cloudflare AI controls

Cloudflare has invested heavily in AI crawler management.

Useful features include:

• identifying GPTBot, ClaudeBot, PerplexityBot, and other AI crawlers
• deciding which bots may crawl
• measuring AI crawler traffic
• experimenting with crawler permissions

This helps publishers understand how AI systems access their sites.

5. Schema App

Many AI systems benefit from structured, machine-readable information.

Adding high-quality schema for:

• authors
• organizations
• articles
• FAQs
• products
• events

can make it easier for retrieval systems to understand content.

What actually increases AI citations?

Across studies and publisher experience, several factors appear to matter more than keyword optimization:

• Original reporting
• First-party data
• Named experts
• Clear sourcing
• Statistics with methodology
• Tables and comparisons
• Frequently updated evergreen pages
• Strong author bios
• Internal linking to topic clusters
• Consistent factual formatting

AI systems tend to favor pages that are easy to extract facts from rather than purely opinion-oriented content.

Attribution is becoming its own discipline

Many publishers are now creating dedicated "citation pages," such as:

• research summaries
• statistics pages
• definitions
• timelines
• benchmark reports
• original datasets

These formats are often easier for retrieval systems to quote and attribute than long narrative articles.

If I were advising a mid-size publisher

A practical stack would be:

• Visibility monitoring: Profound or Peec AI
• Technical SEO & crawl analysis: Cloudflare + Screaming Frog
• Content optimization: MarketMuse or Clearscope
• Structured data: Schema App
• Analytics: Google Search Console alongside server log analysis to understand both traditional search and AI crawler behavior

The most effective strategy is not to "write for AI" in the sense of keyword stuffing, but to publish content that is authoritative, well-structured, and easy for retrieval systems to verify and attribute. Original reporting, transparent sourcing, and machine-readable metadata tend to have a larger impact than stylistic changes alone.