ChatGPT answers, unedited

Yes. If the goal is specifically “help our newsroom produce articles that AI assistants are more likely to retrieve and cite”, there are a few tools worth evaluating. The category is generally called AI search optimization / AEO / GEO.

Tools I’d look at

• semrush.com — probably the most straightforward starting point. Its Content Toolkit analyzes drafts against factors correlated with higher AI citation rates and gives recommendations on structure, clarity, and entity signals. It also tracks visibility across ChatGPT, Gemini, Perplexity and Google AI experiences. Semrush Semrush
• tryprofound.com — particularly interesting for a publisher because it goes beyond optimizing individual articles. It tracks which URLs AI systems cite, which competitors get cited instead, what types of pages are being cited, and can turn citation gaps into content briefs. Its page analysis includes readability, freshness, structure, information density and machine readability. Profound Profound Help Center
• enterprise.semrush.com — worth considering if you're talking about a large newsroom with thousands of stories. It combines prompt data, citation/mention tracking, competitive analysis and content recommendations at enterprise scale. Semrush for Enterprise

For a news publisher, I'd use the software differently than an SEO team would

The interesting question isn't simply “How do we optimize this article for AI?” It's:

“What characteristics do articles that get cited by AI answers have, and what should our editors change before publication?”

For example, you could have the system recommend that a story use:

• a concise answer/summary near the top
• explicit dates and geographic scope
• clearly attributed claims
• original reporting and first-party data
• named people, organizations and events with unambiguous context
• structured Q&A or “what we know / what we don't know” sections where appropriate
• tables/timelines/lists when they make factual information easier to extract
• clear distinction between reporting, analysis and opinion
• frequent updating of developing stories
• links to primary documents and source material

The important caveat is that there isn't a guaranteed “AI-citation format.” These products are largely measuring correlations and observed citation behavior. Profound, for example, explicitly lets publishers examine which types of pages and specific content characteristics are being cited, rather than treating one format as universally optimal. Profound Profound Help Center

If I were building the newsroom workflow

I'd probably combine Profound + your CMS/editorial tooling:

Before assignment → identify questions/prompts where competitors are being cited
↓
During reporting → identify what original information the newsroom can add
↓
Before publication → analyze the proposed article and recommend structural changes
↓
After publication → monitor whether ChatGPT/Google/Perplexity/etc. actually cite it
↓
Weekly → feed successful/unsuccessful patterns back into editorial templates

That last feedback loop is particularly valuable. Instead of telling journalists generic things like “write for AI,” you can discover something much more concrete such as “our explainers with a 75-word factual summary and a dated source box are cited substantially more often than our conventional narrative pieces.”

Given the current pressure on publisher referral traffic—Reach, for example, recently attributed a 46% year-over-year decline in Google referrals partly to AI search experiences—the business case is increasingly about citation visibility plus retaining the reader once they arrive, rather than SEO alone. The Guardian

If you tell me roughly the size of the news site and your CMS (WordPress, Arc, WordPress VIP, custom, etc.), I can narrow this down to 3–5 products and compare their actual newsroom workflow, APIs, pricing, and ability to generate article-format recommendations.

Yes. The category you want is usually called AI visibility / GEO (generative-engine optimization) software. For a news publisher, I’d separate tools that measure citations from tools that can actually recommend how to structure the next story.

Tools worth evaluating

• getvisibly.app — particularly relevant to newsrooms. It focuses specifically on publisher AI visibility and frames the problem around which stories/questions get cited. Visibly
• conductor.com — useful if you already have an enterprise SEO/content operation. Its 2026 publishing research analyzed 13M+ publisher citations across seven AI engines and looks at which page types get cited. Conductor
• yext.com — stronger on measuring and understanding AI citations across ChatGPT, Perplexity, Gemini and Claude. Its 2026 analysis covered 17.2M citations. Yext
• muckrack.com — worth looking at for a newsroom because its GEO guidance is explicitly oriented toward news discovery, citation and attribution rather than generic brand SEO. Muck Rack
• slatehq.com — more measurement/benchmarking oriented; tracks AI visibility across several answer engines and can help establish a baseline for a publisher. Slate

But there's an important distinction

If your requirement is specifically:

"Given this story/topic, tell my editor whether it should be a breaking-news article, explainer, FAQ, timeline, data table, comparison, Q&A, etc., based on which formats AI systems tend to cite."

That's a less mature product category. Most existing platforms measure whether you're being cited rather than acting as an editorial-format recommendation engine.

For a news site, I'd want software with this workflow:

1. Topic detection — identify the questions people are asking AI about a developing story.
2. Competitive citation analysis — query ChatGPT, Perplexity, Gemini, Claude, Google AI Mode, etc., and identify which publications are being cited.
3. Format analysis — classify cited pages as breaking news, explainer, FAQ, timeline, analysis, data visualization, Q&A, live blog, etc.
4. Content-gap analysis — determine what information the cited pages provide that your article doesn't.
5. Brief generation — recommend a structure such as:

• 2-sentence direct answer
• key facts/data
• timeline
• named sources
• methodology
• FAQ
• original reporting
• Post-publication monitoring — repeatedly test the same prompts and show whether the new article starts appearing as a citation.

That last piece matters because citation behavior differs substantially between AI engines; Conductor's research, for example, found persistent differences in source preferences between engines. Conductor

There is also evidence that content format matters: one 2026 experiment reported that comparison/list-style pages represented 51% of its non-homepage citations, although that's a single study rather than a universal rule for newsrooms. Prismic

If I were evaluating vendors for a newsroom

I'd make the RFP less about "GEO score" and ask vendors to demonstrate:

One caution: "AI citation optimization" isn't equivalent to increasing referral traffic. AI assistants can cite a publisher while satisfying the user's question without generating a click. That's already a significant concern for publishers; current reporting describes publishers treating AI visibility as a distinct distribution metric rather than simply another form of SEO traffic. Digiday

If you're building or buying this for a news organization, I can also map out the current vendor landscape and identify which products actually provide editorial/content-format recommendations versus merely reporting AI citation metrics.

For a news site, the software category you’re looking for is usually called AEO (Answer Engine Optimization), GEO (Generative Engine Optimization), or AI visibility/citation tracking. These tools don’t guarantee citations, but they can show which stories, formats, sources, and page structures are being cited by AI assistants and suggest changes to improve “citation eligibility.” CiteFlow HubSpot

Useful options:

For a news publisher specifically, I’d look for features beyond generic “AI SEO”:

• Citation-source analysis: “When ChatGPT answers ‘what happened in X?’ which URLs does it cite?”
• Format analysis: Does AI prefer your:

• breaking news article
• live blog
• FAQ explainer
• timeline
• data visualization
• transcript
• expert Q&A
• original investigation
• Content gap detection: “AI answers this topic every day but rarely cites us.”
• Entity monitoring: Whether your publication, journalists, and recurring coverage areas are recognized correctly.
• AI referral analytics: Whether AI-generated referrals actually convert into engaged readers.

A practical newsroom workflow would be:

1. Pick 100–500 high-value prompts readers ask AI assistants (e.g., “What happened with [topic]?”, “Who is involved?”, “What are the implications?”).
2. Track which publishers are cited.
3. Compare cited articles against your own:

• headline style
• answer placement
• use of structured facts
• original reporting
• update frequency
• Build templates for formats that repeatedly win citations.

Formats that often have strong citation potential because they provide extractable answers include:

• “What we know / what we don’t know” explainers
• timelines
• fact boxes
• definitions
• comparisons
• original datasets
• methodology pages
• expert interviews with clear questions and answers

The field is still evolving, and citation metrics are not yet standardized, so I’d treat these tools as editorial intelligence systems, not as a replacement for audience analytics or journalism judgment. arXiv

For a small-to-medium newsroom, a reasonable starting stack is usually:

• one AI visibility tracker (Profound, Peec, HubSpot AEO, Semrush, etc.)
• your existing analytics platform
• an editorial content audit workflow

For a large publisher, I’d prioritize Profound/Scrunch-style monitoring plus internal experimentation because the main advantage comes from learning which story formats your own audience and topics earn citations for. CiteFlow

Yes. If the goal is specifically “what should our newsroom publish/produce so AI assistants are more likely to cite us?”, I’d look beyond ordinary SEO platforms and toward AI-visibility / AEO platforms that expose citation patterns and content recommendations.

Best fits

• tryprofound.com — probably the strongest fit for a larger news organization. It tracks AI answers/citations and, importantly, connects AI-crawler behavior with content recommendations. It is designed more for an enterprise optimization workflow than simply giving you a “visibility score.” Profound Austin Heaton
• otterly.ai — strong for monitoring which articles/URLs are actually being cited across ChatGPT, Perplexity, Gemini, Google AI Overviews, Copilot, etc. Its research also specifically examines news-publisher citation patterns, making it particularly relevant to publishers. Otterly Otterly
• scrunch.ai — worth evaluating if you want AI visibility measurement tied to publisher traffic. A recent study using Scrunch data found that AI mentions can correlate with subsequent visits even though direct AI referral traffic remains small. Search Engine Land
• hubspot.com — more marketing-oriented, but useful if you want research on which on-page formats earn citations and an easier experimentation workflow. HubSpot Blog

The important distinction for a news site

I'd actually frame the software requirement as:

“Given our existing stories and the questions people ask AI assistants, tell editors which story formats, structures and topics are most likely to become cited sources.”

That's a little different from conventional GEO software, which often answers only “Are we being mentioned?”

For a newsroom, I'd want the system to analyze things like:

• breaking-news article vs. explainer
• FAQ/Q&A vs. conventional article
• “what we know / what we don't know” format
• timelines
• data-driven articles
• original reporting
• local guides
• evergreen explainers
• comparison/list formats
• expert interviews
• structured fact boxes
• update frequency
• headline/question phrasing

That matters because there isn't one universally winning AI content format. A 2026 analysis of 25,000+ citations found that citation patterns vary substantially by industry and page type. DeltaV Digital

There's also a particularly interesting publisher-specific signal: USA Today Co. is reportedly experimenting with restructuring content templates specifically to make reporting easier for AI systems to access, understand and cite. Digiday

If I were building the stack

For a serious news publisher, I'd probably test:

Profound + your CMS/content analytics + an experimentation layer

with the core loop:

AI queries → citations → characteristics of cited stories → recommended format/template → publish → measure citation lift → update recommendation model.

Otterly is a good lower-cost way to establish the citation dataset before investing in something more deeply integrated.

One caveat: citation ≠ traffic. Recent research suggests AI referrals themselves are still a small fraction of publisher traffic, while being mentioned by an AI can potentially influence later direct/search visits. Search Engine Land So I'd optimize for AI citation + subsequent brand/search/direct engagement, not citations alone.

If you're evaluating this as a buy-vs-build product opportunity for a news organization, I can also map out the current vendors and identify what none of them seems to do well yet.

Yes. But I’d separate “software that tells you what to publish” from “software that measures whether AI assistants actually cite you.” The latter is much more mature right now.

For a news publisher, I’d shortlist:

• tryprofound.com — probably the strongest enterprise option. It tracks how often your stories/domain are mentioned or cited across AI answer engines and can show which prompts and competitors drive visibility. Good for a newsroom-wide AI visibility program. Baarely Bloomiro
• scrunchai.com — particularly interesting for publishers because it goes beyond prompt tracking into understanding how AI crawlers interact with your site. I'd investigate this if your question is partly “what should we change in our article templates/technical presentation?” Baarely BeVisible
• peec.ai — a good middle-market choice for systematically testing questions against ChatGPT, Gemini, Perplexity, etc., and seeing which sources are cited. Baarely Bloomiro
• otterly.ai — cheaper/easier way to start measuring AI citation share and competitors before committing to an enterprise platform. Baarely AISEOShift
• semrush.com / ahrefs.com — worth considering if the newsroom already uses one of these for SEO. Their AI-visibility capabilities can connect traditional search performance with AI visibility rather than creating another standalone stack. UX Continuum

The important distinction for a news site

If your actual question is:

“Given this story, should we make it a Q&A, timeline, explainer, data table, FAQ, live blog, etc. to maximize the chance an AI assistant cites it?”

then most AI-visibility products aren't quite the answer yet. They primarily tell you what is being cited, rather than reliably predicting the optimal format before publication. Austin Heaton

There are emerging tools focused more directly on the content-action layer, including platforms such as frase.io, which combines AI-visibility findings with content optimization and publishing workflows. Frase

And there's an important caveat: don't assume that simply turning paragraphs into bullets and tables will make AI assistants cite you. A recent controlled test of 2,400 cases found little effect on citation/accuracy for ChatGPT and Claude, although formatting affected attribution in Perplexity. 5W Public Relations

What I'd build for a newsroom

For traffic retention, I'd actually want a system that scores every story on something like:

AI citation opportunity → recommended format → structural changes → citation monitoring → referral traffic

For example:

Breaking story → “What we know / What we don't know” explainer

Policy story → key provisions + who is affected + primary-source links

Election story → candidate positions comparison + results/data table

Complex investigation → executive summary + findings + methodology + evidence

Developing event → continually updated timeline + timestamped facts

Then measure whether those formats actually increase AI citations and AI-referred sessions, rather than optimizing for a generic “AI score.”

That's becoming particularly relevant to publishers: USA Today Co. is explicitly experimenting with restructuring content formats and templates to make its reporting easier for AI systems to understand and cite. Digiday And the Indian Express Group recently announced a partnership that will surface summaries/excerpts with attribution and links in ChatGPT, illustrating the other side of the equation—AI distribution itself can become a traffic channel. The Indian Express

If you're evaluating vendors for a news organization specifically, I'd start with Profound + Scrunch, then test Peec/Otterly as lower-cost alternatives. If you tell me the approximate size of the newsroom (e.g. 20, 100, or 1,000+ journalists), I can narrow this to 3 vendors and the exact capabilities I'd demand in an RFP.

Yes. For a news publisher, I’d look beyond generic “AI SEO” tools and prioritize platforms that can tell you which article structures/formats are actually getting cited by ChatGPT, Gemini, Perplexity, and Google’s AI experiences.

Best fits

• tryprofound.com — probably the strongest fit for an editorial strategy team. It tracks which sources AI systems cite, categorizes the cited page types, and uses citation data to inform which pages to create, update, or optimize. Its content analysis also evaluates things such as readability, freshness, structure, information density, and machine readability, with specific optimization recommendations. Profound Profound Help Center

Best for: “What kind of article should our newsroom produce to win citations?”

• scrunch.com — particularly useful if you want to study the actual URLs and formats winning citations. It lets you drill from an AI prompt → cited URLs → citation frequency and consistency, and compare your pages with competitors. It also has page/content optimization recommendations. Scrunch Help Center Scrunch

Best for: competitive intelligence and figuring out why another publisher's article is being cited.

• peec.ai — a simpler, more action-oriented option. It identifies the content types surfaced in particular LLMs, shows the most-cited sources, and generates prioritized actions around content gaps and citation opportunities. Peec AI

Best for: giving editors a relatively straightforward “do these things next” workflow.

• getvisibly.app — worth looking at specifically because it's positioned around publishers/newsrooms, rather than adapting a brand-marketing AEO product to journalism. Visibly

Best for: a newsroom-specific AI visibility workflow.

What I'd actually measure for a news site

The interesting product opportunity isn't simply “increase AI citations.” It's identifying the editorial characteristics associated with citation wins:

This matters because AI answers increasingly synthesize multiple sources, rather than simply selecting one “best” article. Scrunch's current analysis, for example, reports an average of nearly 10 sources per AI response and says third-party sources account for the large majority of citations it analyzed. Scrunch

My recommendation

If you're evaluating this as a publisher trying to retain/direct traffic, I'd demo Profound and Scrunch first.

The key question I'd ask each vendor is:

“Can you take our 10,000 articles, identify the formats and structural characteristics of articles that are disproportionately cited by AI assistants for our topics, and turn that into an editorial recommendation before an editor commissions the next story?”

If the answer is yes, that's much more valuable than a dashboard showing that your site was cited 4,200 times.

If you're building this software rather than buying it, there's also a pretty interesting product gap here: a newsroom-facing system that combines AI citation data + article-level content analysis + editorial commissioning recommendations.

For a news site, the software category you’re looking for is usually called AEO (Answer Engine Optimization), GEO (Generative Engine Optimization), or AI visibility/citation tracking. These tools try to show which stories are being cited by AI assistants and recommend changes to improve the chance that your reporting becomes a source. GeoCopy HubSpot Blog

The strongest options for a newsroom workflow:

For a news publisher specifically, I would prioritize tools that answer these questions:

1. Which stories are AI assistants citing today? - Track citations from ChatGPT, Gemini, Perplexity, and AI search experiences.
2. What formats win citations in our category? - Example: explainers, timelines, data tables, “what we know” pages, FAQs, live updates.
3. Which competitors are being cited instead of us?
4. What edits would make an existing article more extractable?

The content formats that these tools should help you test:

• Structured explainers - “What happened?”
• “Why it matters”
• “Key facts”
• “Timeline”
• “What happens next”
• Original data journalism - datasets
• rankings
• investigations
• methodology pages
• Reference pages - election guides
• policy trackers
• company profiles
• biographies
• FAQ-style sections - direct questions with concise answers
• Evidence blocks - named sources
• dates
• numbers
• definitions

Research into GEO suggests AI systems tend to favor pages with clear structure, extractable facts, comparisons, and strong topical alignment rather than simply pages that rank highly in traditional search. arXiv arXiv

A practical newsroom stack might look like:

• Daily editorial intelligence: Profound or HubSpot AEO
• SEO/content research: Ahrefs or Semrush
• Article optimization: Surfer or an internal CMS checklist
• Analytics: track AI referral traffic separately from Google traffic

For a mid-sized news site, I would probably start with Profound + Ahrefs/Semrush, then build an internal “AI citation score” for articles: citations earned, AI prompts won, update frequency, and format type. CiteFlow Conductor

Yes. For a news publisher, I’d look beyond generic “GEO” tools and prioritize software that can answer two questions:

1. Which of our stories are actually being cited by AI assistants?
2. What content formats/topics/structures are associated with higher citation rates, so the newsroom can produce more of them?

The market is still evolving, but these are the most relevant options I’d evaluate:

• getvisibly.app — Probably the closest fit to your use case. It is explicitly built around AI visibility for publishers/newsrooms, including seeing which reporting gets cited and using that to inform editorial strategy. Visibly
• bing.com — Worth implementing regardless of what you buy. Microsoft's AI Performance reporting exposes AI citation counts, page-level performance and “grounding queries” showing queries associated with retrieval. Search Engine Journal
• yext.com — More enterprise/brand-oriented, but its 2026 research analyzes 17.2 million citations across ChatGPT, Perplexity, Gemini and Claude, making its measurement infrastructure interesting if you need large-scale competitive benchmarking. Yext
• slatehq.com — AI-search visibility/benchmarking rather than a newsroom-specific editorial tool. Useful if you want to track citation share across multiple answer engines and establish a recurring benchmark. Slate

What I'd actually want the software to recommend

For your use case, the killer feature isn't simply “your AI visibility score is 14%.” It should produce something like:

For questions about the 2026 housing market, your explanatory timelines are cited 3.2× more often than conventional breaking-news articles.

Recommended format: “What we know / What we don't know” explainer
Recommended additions: original statistics + named primary sources + concise answer near the top
Topics with citation opportunity: 17
Competitor most frequently cited: Reuters
Your pages currently winning: 8

That's important because emerging research suggests there isn't one universally winning format. Citation patterns differ substantially by industry and query type. DeltaV Digital

For a newsroom, I'd therefore build the workflow around content-format × topic × AI-engine × citation-rate, rather than generic SEO recommendations.

Formats I'd test first

For a news site, I'd specifically experiment with:

• Explainers: “What we know about X”
• FAQs: direct question → direct answer
• Timelines
• Data/analysis pieces with original statistics
• “What happens next” pieces
• Comparisons/table-driven stories
• Primary-source breakdowns
• Living/updated explainers
• Original reporting that contains facts unavailable in syndicated coverage

There's evidence supporting that last point particularly strongly: research reported by Search Engine Journal found that syndicated press releases have very little AI citation traction compared with original editorial content. Search Engine Journal

And I'd be careful about simply optimizing everything for AI. The goal should be citation + eventual reader value, because an AI answer can cite you without generating a click.

If I were building this for a publisher

I'd make the product dashboard look roughly like:

AI Citation Intelligence

Then an Editorial Opportunities queue:

12 stories worth producing this week

1. “How the new tax law works” — high citation probability
2. “What the Fed decision means for mortgages” — high
3. “2026 election calendar and key dates” — very high

Why: comparable pages are frequently cited, but your publication has low share of citations for these queries.

That would be much more valuable to an editor than another SEO content score.

One other important consideration: accessibility matters. A 2026 study of major publishers found a striking relationship between paywalls and AI citations, with open-web publishers substantially outperforming hard-paywalled/metered publishers in its test. That's not definitive across all AI systems, but it's something I'd include in the product's experimentation framework. prnewswire.com

If you're evaluating this as a software product to buy/build, I can also map out the current vendors in a publisher-focused competitive matrix (citation tracking vs. content recommendations vs. newsroom workflow vs. pricing) and identify the biggest product gap.

Yes. For a news publisher, I’d look beyond generic “AI SEO” tools and specifically seek software that can answer:

“Given our existing reporting, what page structure/format should we publish so AI assistants are more likely to understand, extract, and cite it?”

That’s becoming a real publishing workflow: USA Today Co., for example, is currently testing ways to reformat website content specifically for AI consumption and citation. Digiday

The tools I’d shortlist

• otterly.ai — probably the easiest starting point. It tracks mentions/citations across ChatGPT, Perplexity, Google AI Overviews, Gemini, Claude and others, and importantly offers page-level AI-readiness/citation recommendations rather than just reporting visibility. Otterly
• tryprofound.com — my pick if you're a larger newsroom. It's stronger for deep citation analytics, competitive intelligence, prompt monitoring and understanding which sources AI systems are choosing. An independent 2026 benchmark found Profound among the most frequently cited AI-visibility platforms. Foglift
• peec.ai — worth considering for a newsroom that wants to identify content gaps and competitor citation patterns across many prompts.
• semrush.com — attractive if your editorial/SEO team already uses Semrush; its AI Visibility capabilities add AI-answer monitoring to a conventional SEO/content workflow. AI Bro
• hubspot.com — less publisher-specific, but its 2026 research is particularly relevant to your question because it analyzes which content formats actually receive AI citations. It found, for example, that comparison content performed especially strongly in ChatGPT, while listicles, articles and category pages were among the strongest overall formats. HubSpot Blog

What I'd actually build for a news site

The interesting opportunity isn't simply “track whether CNN/NYT/etc. gets cited.” I'd want the software to take an article and produce something like:

Then I'd have it monitor whether those pages subsequently get cited and feed the citation results back into the editorial recommendation engine.

That's more valuable than a conventional AI-visibility dashboard because it creates a closed loop:

article → recommended format → publish → monitor AI citations → learn which formats work → update editorial templates.

There is some evidence that this structural approach matters: recent AEO research points toward question-led headings, direct-answer summaries, structured data, statistics, visible update dates and clearly attributed authorship as useful citation signals. HubSpot Blog HubSpot Blog

If you're evaluating this as a product category for a publisher, I'd put Profound + Otterly at the top of the competitive set, but I'd specifically test whether either can recommend editorial formats rather than merely report AI visibility. That distinction could actually be the interesting product gap.

Yes. The category you’re looking for is usually called GEO (Generative Engine Optimization), AI visibility, or AI citation intelligence. For a news publisher, I’d look specifically for software that can answer “What should we publish, in what format, and on which topics, to become a source AI assistants cite?” rather than generic SEO tools.

A few options stand out:

• getvisibly.app — probably the closest fit to your use case. It is explicitly positioned for publishers/newsrooms and measures which articles get cited in AI answers. Its publisher workflow focuses on turning AI citation data into editorial decisions. Visibly
• blogs.bing.com — useful baseline because Microsoft now exposes AI citations, cited URLs and citation trends. It is more measurement than editorial recommendation, but it's effectively free. Bing Blogs Search Engine Journal
• stacker.com — interesting for publishers because its GEO Reporting tracks individual stories across ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini and Claude. It is particularly relevant if you're also thinking about content distribution/syndication. GlobeNewswire Stacker
• meltwater.com — broader enterprise media/AI visibility platform. Its research tracks millions of AI citations and breaks visibility down by source, format and platform. Better suited to a larger newsroom/analytics operation than a lightweight editorial tool. Meltwater
• Temso — worth evaluating if you specifically want a GEO workflow that goes beyond monitoring and recommends/implements content changes. Current industry comparisons position it around publisher/media GEO, although I'd validate its methodology and publisher references before buying. Geosoftware Rankings

What I'd actually want the software to recommend

For a news organization, “make the article more AI-friendly” isn't specific enough. The useful product would analyze your existing citation data and recommend things such as:

For this topic, don't publish another 700-word breaking-news article. Publish a “What we know / what we don't know” explainer with a timeline, key numbers, named sources, and a continuously updated FAQ.

That's because recent research suggests there isn't one universally winning content format. Citation patterns vary substantially by industry and query type. DeltaV Digital

I'd want the system to score each potential story on:

1. Citation opportunity — questions people are asking AI that your newsroom could credibly answer.
2. Format recommendation — breaking story vs. explainer vs. FAQ vs. timeline vs. data visualization vs. comparison vs. live/update page.
3. Citation gap — which competing publications AI currently cites.
4. Evidence gap — facts, numbers, primary sources, expert quotes, etc. that would make your article more useful as an AI source.
5. Update opportunity — existing articles that could be refreshed rather than creating another URL.
6. AI citation probability — measured experimentally across ChatGPT, Gemini, Perplexity, Claude and Google AI surfaces.
7. Traffic value — crucially, whether the AI citation actually sends users back to the publisher.

That last point matters. Citation ≠ traffic. AI systems can cite an article while satisfying the user's question without a click. There is also substantial platform-to-platform variation in which sources get cited. arXiv arXiv

If you're evaluating vendors

I'd make them demonstrate this with your own newsroom data, not a generic GEO score.

Give each vendor, say, 1,000 URLs and ask:

“Show me the 50 stories/topics where changing the content format has the highest predicted probability of increasing AI citations, and explain exactly what you would change.”

Then require them to show before/after citation measurements across multiple AI engines.

For a news publisher trying to retain traffic, I would currently shortlist Visibly + Bing AI Performance + Stacker, and evaluate whether a larger enterprise platform such as Meltwater is justified by your scale. The emerging publisher-specific category is still young, so I'd be wary of vendors promising a guaranteed “GEO score” or guaranteed AI rankings. Recent academic reviews explicitly find that cross-platform, long-term causal evidence for many GEO tactics is still limited. arXiv

If you tell me roughly your site's size (e.g. 10k, 100k, 1M+ articles), CMS, and whether your priority is citations, referral traffic, or subscriptions, I can narrow this to the 3–5 products I'd actually pilot and compare pricing/features.

Yes. For a news publisher, I’d look less at generic SEO tools and more at software that can answer two questions:

1. Which of our stories are being cited by AI assistants?
2. What format/structure should we use for stories that aren’t getting cited?

The market is still immature, but a few options stand out.

Best fits

• OtterlyAI — Probably the best starting point for measurement. It tracks citations and visibility across ChatGPT, Google AI Overviews/AI Mode, Perplexity, Gemini, Claude and Copilot. Its data can show which URLs and topics are winning or losing citations. GlobeNewswire

otterly.ai

• HubSpot AEO tools — More useful for the content-format question. Recent AEO research found that listicles, articles and product/category pages are among the most-cited formats, while comparison content performs particularly well in ChatGPT. It also identifies structural factors such as descriptive headings, statistics, author bios and update dates. HubSpot Blog

hubspot.com

• Visibly — Particularly interesting because it's explicitly positioning itself around newsrooms, rather than brands. It frames the problem as measuring whether a newsroom's reporting is cited for the questions its audience asks. Visibly

getvisibly.app

• Profound — Worth evaluating if you're looking for a more enterprise-oriented AI-search intelligence layer. The broader GEO category is increasingly moving toward prompt-level citation/share-of-voice monitoring rather than traditional keyword rankings. GeoSoftware Rankings

The important part: don't optimize simply for "AI-friendly" articles

For a news site, I'd build a system that analyzes your own citation data and recommends templates, rather than having an LLM rewrite everything.

For example:

That's consistent with what publishers are actually experimenting with. USA Today, for example, is testing different content structures and formats specifically to make its reporting easier for AI systems to access, understand and cite. Digiday

And publisher research suggests Q&A/conversational formats, machine-readable metadata and explicit subject-matter terminology are already among the most common GEO tactics being adopted by news organizations. Digiday

If your real goal is traffic retention

There's an important distinction: getting cited isn't necessarily the same as getting a click.

I'd optimize for "citation → click", not citation volume alone. A useful internal score would be something like:

AI citation opportunity = audience demand × citation probability × click propensity × editorial value

Then your software could tell an editor:

"This topic is being asked 18,000 times/month across AI engines. Competitors are cited in 72% of answers, but your coverage is cited in 8%. Convert the existing 1,400-word article into a 'What changed?' explainer with a 60-word direct answer, timeline, data table and primary-source links."

That's considerably more valuable than an SEO plugin saying "add more headings."

One other reason to be cautious: AI citation behavior is changing rapidly. For example, recent research has found large shifts in which sources ChatGPT cites, so a strategy based on one static list of "AI-friendly formats" can become obsolete quickly. Axios

If I were choosing today: I'd pilot OtterlyAI + a newsroom-specific system like Visibly, then build your own recommendation layer on top of your historical stories/citations. The proprietary advantage would be learning which formats work specifically for your publication, topics and audience, rather than relying on generic GEO advice.

For a news site, you’re looking for AI visibility / Generative Engine Optimization (GEO) tools: software that monitors when AI assistants cite your reporting and helps editors identify formats, topics, and page structures that are more likely to be retrieved and cited. This is a newer category, so no tool can reliably “guarantee” citations, but several can help build an editorial feedback loop. OpenAttribution

Tools worth evaluating

What these tools should help your editors discover

The useful output is not “write more AI-friendly articles.” It is more like:

• Which story formats get cited?

• FAQ explainers (“What happened and why does it matter?”)
• timelines
• data-driven analyses
• original reporting with unique facts
• “what we know / what we don’t know” pages
• comparison and background guides
• Which pages are missing from AI answers?

• Example: AI assistants cite three national outlets for “How will this law affect renters?” while your local reporting has the strongest details. That becomes an editorial opportunity. Visibly
• Which questions your audience asks but your site does not answer directly

• AI systems tend to favor pages that clearly resolve a question, have strong sourcing, and are easy to parse. Visibly TechRadar

A practical newsroom stack

For a mid-sized publisher, I would consider:

1. Bing Webmaster Tools AI Performance (free starting point)

Tracks which URLs are cited in some AI-generated answers and what queries trigger citations. Search Engine Land

2. One dedicated AI visibility tracker

Pick one of Profound, Peec AI, Otterly.AI, or Visibly depending on whether you need enterprise reporting or editorial workflows. OpenAttribution Visibly

3. Your analytics + CMS data

Combine citation data with:

• article type
• author
• beat
• update frequency
• backlinks
• engagement metrics

The goal is to answer: “When an AI assistant needs a source for this question, what kind of story does it choose—and why?”

One caution: AI citation metrics are still noisy. A citation does not always translate directly into referral traffic, and publishers are still figuring out how to measure the value of appearing in AI answers. Search Engine Land Forbes

If you’re designing this for a newsroom, I’d prioritize tools that produce editorial recommendations (“create a FAQ explainer on this topic”) rather than dashboards that only show “share of AI mentions.”

For a news site, you’re looking for AI visibility / Generative Engine Optimization (GEO) software: tools that analyze when AI assistants cite your reporting, identify missed citation opportunities, and recommend structures or formats that make articles easier for AI systems to extract and reference. Visibly Muck Rack

The market is still emerging, but these are the main categories and tools worth evaluating:

1. AI citation tracking platforms (most directly relevant)

• getvisibly.app — built specifically around publisher AI visibility. It tracks whether newsroom content appears as sources in AI answers and helps identify missed opportunities. Visibly

Best for: editorial teams that want “which stories should we rewrite or expand to win AI citations?”

• bing.com — Microsoft has added AI citation insights showing which URLs are referenced in AI-generated answers and which queries lead to citations. Bing Blogs

Best for: a free baseline measurement layer.

• semrush.com — adding AI search visibility capabilities on top of its SEO suite. TechRadar

Best for: publishers already using SEO workflows who want AI visibility integrated with keyword/topic research.

• ahrefs.com — useful for authority, link, and content-gap analysis; increasingly relevant because AI citation tends to correlate with strong sourcing, topical authority, and clear information architecture. TechRadar

Best for: investigative/news publishers with large archives.

2. Content-format recommendation tools

These help answer: “Should this be a Q&A, explainer, timeline, data table, FAQ, comparison, etc.?”

• marketmuse.com — content intelligence platform that recommends topic coverage, structure, and gaps.

Best for: large editorial operations optimizing evergreen coverage.

• clearscope.io — content optimization focused on relevance and comprehensiveness.

Best for: turning newsroom briefs into AI-readable explainers.

• surferseo.com — provides content structure recommendations and optimization workflows. TechRadar

Best for: fast-moving publishers producing high volumes.

3. Editorial analytics + AI workflow platforms

For a newsroom, I would also look at:

• chartbeat.com — audience engagement analytics.
• parse.ly — publisher analytics and content performance insights.

These don’t optimize AI citations directly, but they help connect AI citation → referral traffic → subscription/engagement outcomes.

Formats that software should recommend for AI citation

A good system should push your newsroom toward formats like:

• “What we know / What we don’t know” explainers - AI assistants often need concise factual synthesis.
• FAQ-style reporting - Question headings map naturally to user prompts. Publisher research has found Q&A/conversational formats are among common GEO tactics. Digiday
• Original data stories - Proprietary datasets, surveys, investigations, and methodology pages give AI systems something unique to cite.
• Timelines - Useful for events where assistants need chronological answers.
• Entity pages - “Who is X?”, “What is Y?”, “Background on Z” pages create clear reference points.
• Fact boxes / structured summaries - A short “key facts” section near the top makes extraction easier. Bing Blogs

If I were choosing a stack for a mid-size news publisher

I’d probably combine:

1. Bing Webmaster Tools (free AI citation monitoring) Bing Blogs
2. Visibly or another AI citation tracker (editorial opportunity discovery) Visibly
3. Semrush/Ahrefs (topic authority + competitive gaps)
4. Your CMS analytics (Chartbeat/Parse.ly) to measure whether citations turn into visits or subscriptions.

The key capability to look for is not “AI writing.” It is: “show me the prompts where competitors get cited and we don’t, then tell editors what article structure or missing evidence would make our reporting a better source.” Visibly

For a news site, the emerging category you want to look at is Generative Engine Optimization (GEO) or AI search visibility software. These tools try to answer: “When ChatGPT, Perplexity, Gemini, or AI Overviews answer a question, which publishers get cited, and what content structures make those citations more likely?” TrueCite SitePoint

The strongest options for a newsroom depend on whether you need research, measurement, or content recommendations:

For a news organization specifically, I would prioritize tools that can analyze:

• Citation winners: “When AI answers who/what/why happened?, which URLs does it cite?”
• Format patterns: “Are cited stories usually timelines, explainers, FAQs, data pages, live blogs, investigations, or summaries?”
• Content gaps: “What questions are AI assistants answering where our newsroom has no strong source?”
• Update opportunities: “Which evergreen stories should be refreshed because AI assistants still cite older competitors?”

A practical editorial stack might look like:

1. AI citation monitoring: Profound or similar GEO tracker
2. Editorial optimization: Surfer SEO / content workflow tooling
3. Traditional search intelligence: Ahrefs or Semrush
4. CMS experimentation: Build templates for AI-friendly formats

Formats that many publishers are testing for AI citation potential include:

• “What we know so far” explainers with clear fact blocks
• Chronologies/timelines
• “Key facts” boxes near the top
• Data-driven comparisons
• FAQ sections answering likely reader questions
• Original reporting pages with explicit sourcing and methodology
• Topic hubs that consolidate coverage

One caution: no tool can guarantee that an AI assistant will cite a story. Citation behavior varies by model, retrieval system, freshness, and query wording. Research on GEO suggests that structure, evidence density, and extractable facts can influence citation likelihood, but the field is still developing. arXiv arXiv

For a news site trying to retain traffic, I’d choose a tool that can show “which of our stories are being cited by AI today, which competitors are winning those citations, and what editorial changes might improve the odds” rather than a generic AI-writing assistant.

For a news site, the software category you’re looking for is usually called GEO (Generative Engine Optimization), AEO (Answer Engine Optimization), or AI visibility optimization software. These tools don’t directly “make AI assistants cite you,” but they can analyze what gets cited, identify content gaps, and recommend structures/formats that are more extractable by AI systems. Search Engine Land HubSpot Blog

Useful categories and examples:

1. AI citation tracking platforms (most directly relevant)

These monitor whether your articles appear as sources in AI answers from systems like ChatGPT, Perplexity, Gemini, and AI Overviews.

• tryprofound.com — Tracks brand/content visibility across AI answer engines and helps identify citation opportunities.
• otterly.ai — Monitors AI search visibility and mentions across generative search experiences.
• semrush.com — Expanding into AI visibility tracking alongside traditional SEO tooling.
• ahrefs.com — Useful for content discovery, authority analysis, and understanding what pages earn links/citations.

These are best for answering:

• “Which competitors are being cited instead of us?”
• “Which topics produce AI referrals?”
• “Which article types win citations?”

2. Content intelligence platforms that suggest formats

These are closer to an editorial workflow tool: they recommend article structures, questions to answer, and content improvements.

• marketmuse.com — Content strategy, topic modeling, and briefs.
• clearscope.io — Content optimization and search intent analysis.
• surferseo.com — Content scoring, outlines, and SERP-driven recommendations.
• writer.com — Enterprise content workflows and AI-assisted editorial governance.

For a newsroom, these can help turn:

• “Write another election article”

into:

• “Create a timeline + candidate comparison table + key facts box + FAQ + primary-source links because those chunks are more likely to be extracted.”

3. Publisher-focused analytics stack

For a news organization, I’d combine AI visibility tools with normal audience analytics:

• chartbeat.com — Real-time editorial analytics.
• parse.ly — Audience intelligence for publishers.
• search.google.com — Search performance and technical signals.

Formats that AI assistants tend to favor

Research and industry observations point toward content that is easy for AI systems to extract: clear answers, structured sections, comparisons, lists, and pages containing unique factual information. HubSpot Blog arXiv For a news publisher, tools should help identify opportunities for formats like:

• “What happened?” explainers

• Short answer at the top
• Timeline
• Key players
• What happens next
• Reference pages

• “Everything you need to know about X”
• Definitions
• Background
• Frequently updated facts
• Data journalism pages

• Original datasets
• Charts
• Tables
• Methodology
• Comparison formats

• “X vs Y”
• Policy comparisons
• Election candidate comparisons
• Company comparisons
• FAQ-style modules

• “Why did this happen?”
• “What does this mean?”
• “Who is affected?”

A practical stack for a mid-size news site would be:

1. Profound or Otterly.AI → measure AI citations
2. Semrush/Ahrefs → discover competing sources and demand
3. MarketMuse/Clearscope → generate editorial briefs
4. Chartbeat/Parse.ly → measure whether AI visibility becomes loyal traffic

One important caveat: maximizing citations and maximizing clicks are becoming different goals. Some AI systems can answer users without sending them to the publisher, so a strong strategy also focuses on brand recall, newsletter conversion, subscriptions, and distinctive original reporting, not only citation volume. arXiv

For a news organization, I’d prioritize tools that can ingest your archive and answer: “Which 10,000 existing articles should we repackage into AI-citable formats?” rather than tools focused only on optimizing new posts.

Yes. For a news publisher, I’d look at AI-search/GEO platforms that do more than track mentions—they should connect which stories/formats get cited with recommendations for what your newsroom should produce.

Best fits

• getvisibly.app — probably the closest fit to your use case. It is explicitly positioned around AI visibility for publishers/newsrooms, measuring whether AI assistants cite a newsroom and helping identify what to improve. Visibly
• otterly.ai — strong if you want a broader AI citation-monitoring layer across ChatGPT, Google AI Overviews/AI Mode, Perplexity, Gemini and other engines. Its data is also being used in large-scale publisher citation research. GlobeNewswire
• conductor.com — worth considering if you already have an enterprise SEO/content operation. Its 2026 publishing benchmarks specifically measure citation behavior by AI engine; notably, Google AI Mode accounted for 54.3% of publishing citations in its dataset. Conductor
• yext.com — more enterprise-oriented, but useful for understanding why different AI engines select sources. Its 2026 analysis covered 17.2 million citations and emphasizes structured, authoritative source data rather than conventional SEO alone. Yext

What I'd actually want the software to recommend

Don't optimize simply for "put everything in bullets." Recent controlled testing found that tables/bullets had little effect on citation likelihood in ChatGPT and Claude, although formatting affected attribution in Perplexity. 5W PR

For a newsroom, I'd want a system that scores each potential story/template on things like:

• Citation potential: Is this likely to answer a question an AI assistant receives?
• Fact density: Can an assistant extract specific claims, statistics, dates and names?
• Originality: Does the article contain reporting/data that isn't easily replicated elsewhere?
• Freshness: Particularly important for breaking/current-events queries.
• Source clarity: Are claims, sources and attribution explicit?
• Structure: Can an AI retrieve a concise answer without having to infer it?
• Updateability: Can the article become the canonical living page as a story develops?
• Citation → traffic conversion: Does the cited page give the reader a reason to click rather than merely answering the question?

That last metric is particularly important for your goal. AI citation and traffic are not the same thing: an AI answer can satisfy the reader without a click. Visibly

There's also evidence that content access itself matters enormously for publishers. One 2026 study found hard-paywall/metered publishers received substantially fewer AI citations than open-web publishers, although I'd treat that result cautiously because it was a relatively small query set. PR Newswire

If you're evaluating vendors

I'd specifically ask each vendor to demonstrate:

"Take our last 1,000 articles. Tell me which content formats, article structures, topics and update patterns correlate with citations in ChatGPT, Gemini, Perplexity and Google AI Mode—and then recommend templates for our newsroom."

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

If you're building or buying this for a news organization, I can also give you a shortlist of 5–10 products, with pricing, publisher-specific capabilities, APIs, and whether they actually recommend content formats vs. merely monitor citations.

Yes. For a news publisher, I’d look beyond generic “AI SEO” tools and prioritize software that can analyze which of your stories/pages get cited, identify the formats of pages AI assistants cite, and turn that into editorial recommendations.

Best fits

• tryprofound.com — probably the strongest enterprise option. It tracks AI visibility/citations across major answer engines and can help identify the queries and sources driving visibility. Good if you have a sizable newsroom and want analytics rather than just an SEO plugin. Independent 2026 testing found Profound particularly strong for cross-engine visibility. KAVIO Lyra
• otterly.ai — a more accessible option for monitoring which articles/domains get cited by ChatGPT, Perplexity, Gemini, Google AI surfaces and Copilot. It explicitly offers content audits, content briefs and on-page GEO recommendations. Otterly
• scrunch.ai — worth evaluating if you want ongoing monitoring and alerts around AI visibility. It's more oriented toward monitoring/optimization than newsroom-specific editorial planning. Lyra
• semrush.com — useful if the newsroom already uses Semrush. Its AI-visibility functionality can be combined with conventional search/content data, which is valuable for determining whether an AI-citation opportunity is also a worthwhile traffic opportunity. Austin Heaton

The important distinction for a news site

Most of these products answer:

“Are AI assistants citing us?”

What you actually want is closer to:

“What should our newsroom publish, and in what format, so that AI assistants are more likely to cite us while the resulting pages still attract readers?”

That requires analyzing the cited page itself. For example, you might discover that AI engines disproportionately cite your:

• “What we know so far” explainers
• continuously updated live/rolling stories
• concise fact boxes
• timelines
• “X explained” pieces
• data-driven charts/tables
• Q&A sections
• original reporting with clearly attributable facts
• comparison/ranking pages

There is emerging software specifically trying to turn citation data into these kinds of format recommendations. The Rank Masters

If I were building the stack for a publisher

I'd probably test Profound + your existing analytics/CMS data, rather than buying an “AI SEO” tool in isolation.

The workflow I'd want is:

AI prompts → cited competitors → cited URLs → page structure/format → your historical traffic/engagement → editorial recommendation

For example:

“For queries about the 2026 Maine housing market, AI assistants cite competitors' pages 42% more often when they contain a 5-point summary followed by a data table. Our equivalent stories use long narrative intros. Recommend converting future stories to the summary + data-table format.”

That's much more valuable to a newsroom than a generic “GEO score.”

One other reason to be careful: being mentioned by an AI isn't the same as being cited. A citation with a link is much more directly connected to referral traffic. Current AEO tooling increasingly separates mentions, citations, share of voice and prompt coverage for exactly this reason. Austin Heaton

If your goal is specifically retaining traffic rather than merely increasing AI visibility, I can also give you a shortlist of 5–10 tools/products that are particularly suited to publishers, including which ones actually recommend article formats rather than simply measuring citations.

Yes. For a news publisher, I’d look beyond generic SEO software and specifically at AI-search/GEO platforms that show which pages and content formats are actually being cited by ChatGPT, Gemini, Perplexity, Google AI Overviews/AI Mode, etc.

Best fits

• otterly.ai — probably the most directly relevant starting point. It monitors citations across major AI search engines and has content audits, citation-potential scoring, content briefs, and recommendations for making pages more likely to become AI sources. Otterly

Best for: an editorial team wanting actionable recommendations at the page/content level.

• tryprofound.com — stronger enterprise option for tracking AI citations, prompts, competitors and changes in AI visibility. It is particularly suited to large organizations that want deep citation analytics. Bloomiro TechnologyAdvice

Best for: a large newsroom with an SEO/analytics team and lots of content.

• semrush.com — worth considering if the publisher already uses Semrush. Its AI visibility tooling can connect conventional search data with AI-search visibility rather than creating a separate analytics stack. Austin Heaton

Best for: publishers wanting SEO + AI-search measurement together.

• peec.ai — useful for tracking prompts, competitors and AI visibility, particularly for teams that don't need the full enterprise complexity of Profound. Bloomiro

Best for: mid-sized teams experimenting with an AI-search editorial strategy.

The important distinction for a news site

I wouldn't buy a tool merely because it gives you an "AI visibility score." You want one that can answer:

"For stories on topic X, what kinds of pages are AI engines actually citing, and what characteristics do those pages have?"

For example, you want to discover whether AI systems preferentially cite your:

• breaking-news articles
• explainers
• Q&As
• timelines
• "what we know / what we don't know" pages
• data-driven stories
• original reporting
• interviews
• rankings/lists
• comparison pages
• evergreen backgrounders

That's especially important because citation behavior is changing quickly. One recent publishing benchmark found that educational queries generated the most citations, followed by recommendations and comparisons, while Google AI Mode accounted for more publishing citations than ChatGPT and Perplexity combined in that dataset. Conductor

There's also evidence that news citations are highly concentrated: a recent analysis of 129 million AI citations found news represented only 7.2% of citations overall, with a relatively small group of publishers capturing a large share. GlobeNewswire

If your actual goal is retention

I'd build the workflow around "citation → click → second pageview," rather than citation volume alone.

For each article, measure:

Topic → format → AI citations → referral traffic → engaged sessions → recirculation

Then have the software/editorial system identify patterns such as:

"Stories about elections that contain a 5-question FAQ + source links + continually updated timeline are 2.4× more likely to be cited and generate 18% more AI referral sessions."

That's much more valuable than an abstract GEO score.

My shortlist: I'd demo Otterly first, Profound for enterprise, and Semrush if you're already a Semrush customer. The key question for each vendor should be: "Can you show me which content formats correlate with citations for publishers—not just whether my domain was mentioned?"

Yes. The category you want is generally called AI visibility / GEO (Generative Engine Optimization) software. For a news publisher, though, I’d look beyond generic SEO tools: the useful product should identify which story structures, pages, and formats are actually being cited by ChatGPT, Perplexity, Gemini, Google AI Overviews, etc.

There’s a real shift happening here: USA Today Co. is currently testing different content formats/templates specifically to make reporting easier for AI systems to understand and cite. Digiday

My shortlist

• scrunch.com — probably the most interesting fit if you want to understand what AI systems are actually consuming. It tracks citations, AI-bot traffic, referrals, competitive visibility and the sites AI systems use as sources. Scrunch Scrunch

Best for: discovering content gaps and measuring whether changes to article formats improve AI visibility.

• tryprofound.com — strongest enterprise-oriented option for measuring visibility across AI engines and understanding which sources appear in AI answers. Independent 2026 testing has found particularly broad AI-engine coverage. KAVIO

Best for: a large newsroom wanting an enterprise AI-search intelligence layer.

• peec.ai — more lightweight AI-search monitoring and competitive intelligence. Useful if you want to test a set of newsroom topics/prompts and see which publications are getting cited. Cognizo

Best for: a smaller editorial/product team experimenting with GEO.

• otterly.ai — another relatively accessible option for monitoring citations and AI-search visibility. It becomes particularly interesting for publishers when combined with referral/citation analysis. A recent study involving Otterly analyzed 129 million AI citations across seven platforms. GlobeNewswire

Best for: relatively inexpensive experimentation and measurement.

• getvisibly.app — worth investigating specifically because it's positioning itself around publishers/newsrooms, rather than adapting a B2B marketing product to journalism. Visibly

Best for: a newsroom-centric workflow.

The important distinction

If your question is specifically:

“What should our editors change about an article so AI assistants are more likely to cite it?”

I'd prioritize a system that can answer something like:

“For this topic, AI assistants currently cite 8 competitors. Their cited pages tend to contain a concise answer near the top, structured timelines, named sources, original statistics and FAQ-style explanations. Your article lacks those elements. Here are the three template changes most likely to close the gap.”

That's more valuable than simply knowing that your publication got cited 12% of the time.

I'd therefore evaluate vendors on four capabilities:

And I'd add a fifth requirement: AI crawler/agent analytics. Scrunch, for example, distinguishes AI-bot traffic from conventional traffic. Scrunch

One big caveat for publishers

Don't optimize purely for being cited. You ultimately care about retaining audience and revenue.

AI answers can satisfy the reader without a click, so there's a potentially nasty tradeoff: making an article exceptionally easy for an AI to summarize may increase citations while reducing visits. That's why I'd measure:

AI citation rate → AI referral rate → engaged sessions → newsletter/subscription conversion

rather than treating citation volume as the end goal.

There's also evidence that access matters enormously: one 2026 study found substantially higher citation rates for open-web publishers than hard-paywall/metered publishers, although I'd treat that particular study cautiously because it's industry research rather than independent academic research. PR Newswire

If I were evaluating this for a news organization today, I'd start with Scrunch + Profound demos, and specifically ask each vendor to demonstrate “story format recommendations” rather than giving you a generic AI-visibility dashboard. The market is still evolving quickly, and the recent USA Today experiments suggest publishers are beginning to treat AI-readable content formats as a product/distribution problem of their own. Digiday

If you tell me roughly the size of the newsroom (e.g. 10, 50, 500+ editors) and whether your priority is traffic, subscriptions, or ad revenue, I can narrow this to 2–3 products and compare their capabilities/pricing.

Yes. For a news publisher, I’d look beyond generic “AI SEO” tools and prioritize software that can answer two questions:

1. Which of our stories/formats are actually being cited by AI assistants?
2. What should the newsroom produce or change to increase citation probability?

A few options stand out in 2026:

• getvisibly.app — Probably the closest fit to your use case. It is explicitly aimed at publishers/newsrooms and frames AI visibility around which questions cause AI systems to cite your reporting. It can help identify citation opportunities rather than simply measuring traditional search rankings. Visibly
• otterly.ai — Stronger as a measurement/competitive-intelligence layer. It tracks citations and mentions across ChatGPT, Google AI Overviews/AI Mode, Perplexity, Gemini, Claude and Copilot. A recent study using its data analyzed 129 million AI citations, including news publishers. GlobeNewswire
• bing.com — Worth implementing regardless of what paid platform you choose. Microsoft's AI Performance dashboard reports citations, cited URLs and the queries used to retrieve your content in Copilot/Bing AI experiences. That's particularly useful for finding which individual articles are winning citations. Bing Blogs
• searcheye.io — More interesting if you want the software to turn citation analysis into editorial recommendations. Its publisher guidance focuses on putting answers first, associating claims with named sources, entity clarity and freshness. SearchEye
• shadow.inc — Useful if you want to investigate the characteristics of content that gets selected as an AI source. Its research looks at content structure, sourcing, statistics, multimodal content and semantic completeness. Shadow

What I'd actually build for a newsroom

If your goal is retaining traffic, I'd avoid a tool that simply says “your GEO score is 72.” You want an editorial recommendation engine.

For every story, it should generate something like:

This story has low AI citation potential.

Competitors being cited for this topic tend to have:

• a 40–60 word direct answer near the top
• a fact table
• named primary sources
• original statistics
• explicit dates/timestamps
• FAQ-style subheads

Recommended format: “What we know / What we don't know” explainer + timeline + source table.

Citation opportunity: 17 high-volume questions where competitors are currently being cited.

That is much more valuable to an editor than traditional SEO recommendations.

There's also an important caveat: don't optimize solely for AI citation at the expense of human traffic. Some current research suggests formats containing original data, statistics, clear sourcing and multimedia can perform particularly well in AI retrieval, while publishers with hard paywalls can face substantial retrieval disadvantages. Shadow PR Newswire

And I'd explicitly track citation → click → return visit → subscription/newsletter conversion, because being cited by an AI assistant can otherwise become a vanity metric.

If you're evaluating vendors for a newsroom, my shortlist would be Visibly + OtterlyAI + Bing Webmaster Tools, with SearchEye/Shadow worth investigating for the actual “what should our journalists produce?” recommendation layer.

Yes. For a news publisher, I’d look beyond generic “AI SEO” tools and prioritize software that can connect AI citation data → specific articles → content-format recommendations.

Best fits

• otterly.ai — probably the best starting point. It tracks which URLs are cited across ChatGPT, Google AI Overviews/AI Mode, Perplexity, Gemini, Copilot and Claude, and has a gap analyzer showing prompts where competitors are cited but you aren't. Otterly

Useful for: determining which topics and existing stories deserve a different format.

• tryprofound.com — stronger choice for a larger newsroom/enterprise team. Its value is deeper AI-search monitoring and analysis of what appears in AI answers. The broader 2026 GEO-tool market puts Profound among the leaders alongside Otterly and Semrush. Foglift MarketerHire

Useful for: building an editorial intelligence layer around AI visibility.

• getvisibly.app — particularly interesting because it's explicitly designed for publishers/newsrooms, rather than adapting a brand-marketing GEO product. It frames the problem around which newsroom reporting gets cited for audience questions. Visibly

Useful for: a publisher that wants editorial teams—not just SEO/marketing—to use the data.

• bing.com — worth implementing regardless of which commercial tool you choose. Microsoft's new AI Performance reporting shows citation counts, cited URLs and the queries that led AI systems to retrieve your content. Bing Blogs

Useful for: free first-party evidence about what is actually being cited.

The important distinction

Most GEO tools today answer:

“Are we being cited?”

What you're asking for is more valuable:

“Given what AI assistants cite in our category, should this story be a live blog, FAQ, timeline, data table, explainer, Q&A, analysis, original dataset, etc.?”

That's a less mature product category. Current research suggests there are measurable differences among content formats, and some studies specifically test formats such as listicles, long-form analysis, tables and primary research against AI citation rates. Rank Monster

For a news site, I'd therefore build the workflow roughly like this:

AI citation monitoring → identify winning/losing stories → classify format → compare against competitors → recommend format before publication → measure citation lift afterward.

And I'd make original reporting, named sources, statistics/data, clear answer-first passages and easily extractable facts part of the scoring—not simply “write more SEO content.” Some recent publisher-focused research is finding that AI systems favor content that makes claims easy to identify and verify. Shadow SearchEye

One other caveat: AI citations are volatile. For example, recent data showed a major change in ChatGPT's citation of Reddit within weeks, illustrating why a newsroom shouldn't optimize around one assistant or one fixed “GEO formula.” Axios

If I were evaluating vendors for a newsroom, I'd shortlist Visibly + Otterly + Profound, and use Bing Webmaster Tools as the first-party baseline. The key procurement question would be: “Can your system recommend an editorial/content format from observed citation patterns, rather than merely report citations?”

For a news publisher, the software category you’re looking for is usually called Generative Engine Optimization (GEO), Answer Engine Optimization (AEO), or AI visibility/citation tracking. These tools do not guarantee citations, but they can identify which stories, structures, and signals are more likely to be selected by AI assistants. Muck Rack HubSpot Blog

Here are the main options by newsroom use case:

1. AI citation monitoring (find out what AI assistants already cite)

• tryprofound.com — Tracks brand/topic visibility across AI answers and helps teams understand where they appear or are missing.
• peec.ai — Monitors AI search visibility and citations across major AI answer engines.
• scrunch.ai — Focuses on AI search visibility and how AI systems represent a brand or site.
• otterly.ai — Tracks mentions and citations in AI-generated search results.

These are useful for questions like:

• “When someone asks ChatGPT about our election coverage, are we cited?”
• “Which competitors are replacing us as sources?”
• “Which topics generate AI referrals?”

Cognizo SitePoint## 2. Content optimization tools (suggest formats likely to be extracted)

• marketmuse.com — Helps identify content gaps, topic authority, and article structures.
• clearscope.io — Optimizes articles around search intent and content completeness.
• frase.io — Useful for briefs, question-based structures, and answer-focused formats.
• surferseo.com — Helps editorial teams optimize article structure and coverage.

For news sites, these are most useful for evergreen content:

• explainers
• “what happened / why it matters” pieces
• backgrounders
• comparison pages
• timelines

Timothe AI## 3. Publisher-specific AI optimization tools

• indexly.ai — Built specifically around news/media workflows, including structured news content, citation signals, and AI discovery. Indexly

A newsroom would typically use this type of tool to decide:

• Should this breaking story become a timeline?
• Should we add a “key facts” block?
• Should we publish a glossary/explainer alongside it?
• Which articles are worth updating for AI retrieval?

Formats that AI assistants tend to favor

Regardless of software, tools usually push publishers toward formats like:

• Answer-first ledes

“The bill passed Tuesday because…”

• Key facts boxes

• Who
• What
• When
• Where
• Why it matters
• Structured explainers

• “What is X?”
• “Why is X happening?”
• “What happens next?”
• Original data pages

• charts
• databases
• rankings
• election trackers
• Expert/source transparency

• named reporters
• primary documents
• clear citations
• update timestamps

These structures make it easier for AI systems to extract a concise, attributable answer. Muck Rack Search Engine Land

A practical stack for a mid-sized news organization

A reasonable setup would be:

1. AI visibility tracker: Profound or Peec AI
2. Editorial optimization: MarketMuse/Clearscope/Frase
3. Technical SEO + structured data: existing CMS SEO tooling + schema validation
4. Analytics: measure AI referral traffic separately from Google search traffic

The key metric is shifting from “rank position” to:

• citation share
• number of AI answers mentioning your publication
• referral clicks from AI platforms
• which article formats win citations

Muck Rack SitePointFor a newsroom specifically, I would prioritize citation monitoring + editorial format recommendations over generic AI writing tools. The strategic question is less “how do we publish more?” and more “which types of journalism become the source an AI assistant trusts enough to quote?”

For a news site, you’re looking for AI search visibility / GEO (Generative Engine Optimization) tools rather than traditional SEO tools. The useful ones don’t just suggest “write more articles”; they analyze which pages AI assistants cite, what formats are being extracted, and what content gaps exist. CiteFlow Toolradar

Some options:

For a news organization specifically, I’d prioritize tools that answer these questions:

1. Which article formats get cited?

• “Explainer + timeline”
• “FAQ pages”
• “Data-backed analysis”
• “Original reporting with named sources”
• “Definitions and backgrounders”
• “Comparison pieces”
• “Live trackers”
• Which queries are you missing?

Example:

• “What happened in the UK election?”
• “Why did interest rates change?”
• “Who is [person]?”

AI systems often prefer pages with clear factual blocks that can be extracted.

3. Which pages are already citation magnets?

A good workflow is:

• Find your top AI-cited stories
• Identify their structure
• Create editorial templates around those patterns

A practical stack for a mid-sized news site might be:

• AI citation monitoring: Profound or Peec AI
• SEO/content intelligence: Semrush or Ahrefs
• Editorial workflow: AirOps or your existing CMS tooling
• Custom dashboard: combine AI referral traffic (from analytics) + citation tracking

One caution: nobody can guarantee “get cited by ChatGPT” through a tool. AI citation behavior changes and depends on relevance, authority, freshness, accessibility, and the model’s retrieval process. The tools are mainly useful for finding patterns and improving the odds. axios.com arxiv.org

For a newsroom, the most valuable feature is probably not a writing assistant — it’s a “citation intelligence” dashboard showing which story structures and source types AI systems are rewarding.

Yes. For a news publisher, I’d look less at generic “AI SEO” tools and more at platforms that can answer: “For this topic/query, what kinds of pages are AI assistants actually citing, and what format should our newsroom produce?”

Best fit: Profound

tryprofound.com is probably the closest match to what you described.

Its current product specifically includes “Smart Format Recommendations”: it analyzes top-cited pages for a topic and recommends the content format most likely to earn citations. It also tracks which content types subsequently earn citations, creating a feedback loop for future content strategy. Profound

It can also generate data-backed content briefs based on AI-search responses, citations, user prompts, and AI traffic, including suggested headings/sections and facts already being cited. Profound

For a newsroom, that could translate into recommendations such as:

• “Write this as a FAQ/explainer rather than a conventional article.”
• “Create a live/update page because AI systems repeatedly cite pages answering the latest status.”
• “Add a key-facts box and explicit chronology.”
• “Create a standalone who/what/why explainer linked from the breaking-news story.”
• “This question is currently being answered using competitor sources; build a page specifically addressing it.”
• “AI systems cite tables/data pages for this topic more often than narrative articles.”

Strong alternative: Scrunch

scrunchai.com is particularly interesting if your objective is optimization of an existing publisher site, rather than just editorial research.

Its Site Maps product can show AI-agent traffic, citations, AI referrals and page-level audit scores, and it can surface pages receiving AI-bot traffic but relatively few citations—the kind of pages that may be worth restructuring. Scrunch Help Center

More importantly for your question, Scrunch's Content Gaps feature identifies questions being asked of AI that your site doesn't adequately answer, and lets you choose whether to address the gap with a new article, FAQ section, or update to an existing page. Scrunch Help Center

Its Optimizer also gives concrete structural recommendations such as clearer summaries, FAQs and other changes intended to make pages easier for AI systems to extract and cite. Scrunch

How I'd evaluate them for a news organization

My first demo would be Profound, specifically asking them to demonstrate the workflow on 20–50 of your actual news topics. Ask them to show you before publication how the system determines whether a topic should become a breaking-news article, explainer, FAQ, data page, timeline, analysis, etc.

The important distinction is that you don't really want software that simply says “make your article more AI-friendly.” You want software that learns from actual citation behavior and tells your editors what content to make, what structure to use, and which questions to answer.

And there is a potentially interesting retention strategy here: AI citation optimization and traffic retention aren't necessarily the same objective. I'd design the system to maximize citations → click-through → second pageview, rather than optimizing citations alone. A publisher could deliberately make the cited passage highly useful while creating strong internal pathways to deeper reporting.

If you tell me roughly the size of the newsroom/site (e.g. 50k, 500k, 5M monthly visits) and whether you're looking for software to buy or evaluating the market for building this capability yourself, I can narrow this to the 3–5 most relevant vendors and explain what each actually does for publishers.

For a news site, you’re looking for GEO (Generative Engine Optimization), AEO (Answer Engine Optimization), and AI citation intelligence tools rather than ordinary SEO tools. The goal is not just ranking in Google, but understanding which stories, formats, structures, and facts are being selected as sources by ChatGPT, Gemini, Perplexity, Claude, and AI search summaries. muckrack.com

Useful categories and examples:

For a publisher specifically, I would prioritize tools that answer questions like:

• “When an AI assistant answers ‘what happened with X?’ which outlet gets cited?”
• “What article formats win citations: breaking news, explainers, timelines, FAQs, data stories, analysis?”
• “What facts or sections are AI systems extracting from competitors but ignoring from us?”
• “Which evergreen stories should we update because AI assistants still cite older pages?”

Formats that these systems tend to favor are usually:

1. Fact-first explainers
   • Short answer/dek at the top
   • Clear “what happened / why it matters / what happens next” sections

2. Structured reference pages
   • Timelines
   • Backgrounders
   • “Everything we know so far” pages
   • Glossaries

3. Original reporting with explicit evidence
   • Named sources
   • Data tables
   • Primary documents
   • Expert quotes

4. Frequently updated live stories
   • Visible timestamps
   • Update blocks
   • Clear separation between confirmed facts and speculation

5. Entity-rich journalism
   • Clear author pages
   • Organization information
   • Structured metadata such as NewsArticle schema

These signals help AI systems identify who produced the information and whether it is a trustworthy source. muckrack.com

A practical stack for a mid-sized newsroom might look like:

• AI citation monitoring: Profound or Scrunch AI
• Editorial optimization: MarketMuse/Clearscope/Surfer
• Technical SEO + authority: Ahrefs or Semrush
• CMS experimentation: internal dashboards tracking AI referrals, citations, and subscriber conversions

One caution: no tool can reliably “guarantee” AI citations. The strongest systems are measurement tools that reveal patterns, then help editors test formats. timothe.ai

For a news publisher, I’d evaluate vendors by asking for a demo showing:

1. your own URLs cited in ChatGPT/Perplexity/Gemini answers,
2. competitor citation gaps, and
3. specific recommended story rewrites or new formats—not just an “AI visibility score.”

Yes. The category you’re looking for is usually called Answer Engine Optimization (AEO) or Generative Engine Optimization (GEO) software. For a news publisher, I’d prioritize tools that do more than measure mentions—they should tell editors which topics/pages/formats are likely to earn AI citations.

Best fit: tryprofound.com

Profound is probably the closest match to your use case. It can:

• Track which of your URLs are cited by ChatGPT, Gemini, Perplexity, Google AI Overviews and other answer engines.
• Show which competitor pages are getting cited when yours aren't.
• Break citation performance down by topic, prompt, platform and individual page.
• Analyze a page's structure, freshness, readability, information density and machine readability, then provide optimization recommendations with projected score impact.
• Turn citation gaps into content briefs/workflows. Profound Profound Knowledge Base
• Track which third-party publishers and authors are frequently cited, which is particularly interesting for a news organization trying to understand the formats and sources AI systems prefer. Profound

That makes it more useful for an editorial team than a conventional SEO rank tracker.

What I'd actually use it for at a news site

Instead of asking, "How do we optimize this article for AI?", I'd have the software analyze patterns such as:

• Explainers vs. breaking-news stories
• FAQ-style articles vs. conventional narrative articles
• "What happened?" vs. "Why it matters?" vs. "What happens next?"
• Articles containing original data, timelines, tables or statistics
• Live blogs vs. frequently updated evergreen pages
• Short answer-first summaries vs. long-form reporting
• Original reporting vs. aggregation
• Named experts/sources and primary-source links
• Headlines/question phrasing that corresponds to prompts people give AI

Then correlate those formats with citation share and referral traffic.

That's important because there isn't a single magic "AI-friendly article format." AI citation behavior is changing—recent analysis, for example, found substantial shifts in which domains ChatGPT cites, underscoring how dynamic GEO is. Axios

A useful editorial KPI stack

I'd measure four things separately:

Profound specifically provides citation share, top cited pages, citation relationships and watched-page tracking. Profound Knowledge Base

One caveat: I wouldn't buy a GEO platform solely on its recommendations. The more interesting product for a publisher is the combination of AI citation data + your historical content-performance data + editorial recommendations. That lets you discover your site's winning formats rather than blindly following generic AEO advice.

If you're evaluating this as a publisher product/business problem, I can also compare Profound vs. other GEO/AEO platforms specifically for a news organization, including which ones can actually recommend new story formats rather than just report AI visibility.

Yes. The category you want is usually called GEO (Generative Engine Optimization), AI visibility, or AEO (Answer Engine Optimization). For a news publisher, I’d look specifically for software that can answer “Which formats/topics are getting our reporting cited by ChatGPT, Gemini, Perplexity, etc., and what should we publish next?” rather than just giving you an AI-search rank score.

My shortlist for a news site

Current comparisons put Profound toward the enterprise end, while Otterly is much cheaper; Scrunch is positioned around enterprise AI visibility and content delivery. www.geosoftwarerankings.com

The important distinction

I'd not buy a tool simply because it says it "optimizes content for AI."

For a news organization, the valuable workflow is:

Your articles → AI queries → which articles get cited → what attributes they share → recommended next formats → publish → measure citation lift

For example, the software should be able to discover something like:

Your political explainers are cited 3.2× more often than breaking-news stories. Articles containing a concise answer, original statistics, named experts, timelines and comparison tables have the highest citation rate.

That's much more useful than an arbitrary "GEO score."

There is evidence that content structure matters, but the space is still evolving. One 2026 media-focused analysis reports comparison/analysis formats outperforming other formats for AI citations, while noting that breaking news tends to be less durable as a citation source. www.llmreach.ai And recent reporting shows that citation patterns themselves can shift substantially over short periods, so you want software measuring your actual audience/topic ecosystem, not a universal recipe. www.axios.com

If I were evaluating this for a newsroom

I'd run a trial with Profound + LLMReach + Frase:

• Profound: What is AI citing right now?
• LLMReach: What does this look like specifically for publishers/media?
• Frase: How do we make an individual story more quotable?

If your goal is specifically “tell our editors what type of article to commission next to maximize AI citations while still generating traffic,” I'd put Profound or LLMReach at the top of the evaluation rather than a conventional SEO content optimizer.

There is also an interesting newer category of tools that actually recommends standard vs. comparison vs. listicle formats based on AI-search behavior, rather than merely monitoring citations. GeoCopy, for example, explicitly offers those formats and incorporates AI-search/GEO checks into the publishing workflow. www.geocopy.io

If you tell me roughly what kind of news site (local, national, business, sports, tech, etc.) and your monthly article volume, I can narrow this to the 3 best platforms and compare their pricing, APIs, CMS integrations, and ability to recommend formats automatically.

Yes. The category you want is generally called Generative Engine Optimization (GEO), AI Search Optimization, or Answer Engine Optimization (AEO). For a news publisher, I’d look for software that does more than generic SEO: it should tell editors which story formats, questions, entities, and structures are actually earning citations in AI answers.

Tools I’d evaluate

For a news site, I'd prioritize Visibly

The important distinction is that "what content gets cited by AI?" is not the same question as "what content ranks well in Google?"

For example, recent publisher research points toward formats such as:

• Q&A / question-led stories
• "What we know" explainers
• timelines
• comparisons
• fact/data pages
• original reporting with highly specific claims
• evergreen explainers that get continually updated

Publisher surveys also indicate that Q&A formats and machine-readable metadata are among the strategies news organizations are already using for GEO. digiday.com

And Google's own guidance is important here: it says existing SEO fundamentals remain foundational for generative search, while emphasizing valuable, unique, non-commodity content rather than trying to game an AI system. muckrack.comgeoscout.prowww.deltavdigital.comdevelopers.google.com

The product capability I'd actually want

If you're evaluating software for a newsroom, I'd make the requirement something like:

Given our existing stories and audience/search demand, recommend which content format we should produce next to maximize the probability of being cited by ChatGPT, Gemini, Perplexity and Google AI features—and show the evidence.

Ideally the system would ingest your archive and produce something like:

Topic: Arizona housing insurance
Current format: breaking-news article
AI citation opportunity: High
Recommended derivative: "What Arizona homeowners need to know about X" explainer
Questions to answer: 8
Facts/data to surface: 12
Sources/entities to cite: 7
Competitors currently cited: 4
AI engines where gap exists: ChatGPT + Perplexity
Recommended update cadence: 30 days
Expected traffic value: $$$

That is much more valuable to a publisher than a generic "add more keywords / improve your meta description" SEO tool.

One important caveat: there isn't a proven universal "AI-citable format." A recent 25,000+ citation study found that citation patterns vary substantially by industry, which argues for software that learns from your competitors, topics and audience rather than giving generic GEO advice. www.deltavdigital.com

If you're building or buying this software for a news organization, I can also give you a competitive landscape of ~10 GEO/AEO products, their features, pricing, API availability, and which ones are actually suitable for publishers.

Yes. The category you want is AI visibility / answer-engine optimization (AEO/GEO) software for publishers. The important distinction is that generic SEO tools tell you how to rank in Google; these tools try to measure which stories AI assistants cite and what characteristics those stories have.

Best fits for a news publisher

For example, Presenc AI says its platform tracks publisher citation share across AI platforms and can show which news sources are being cited in a publisher's subject area. presenc.ai

Visibly is particularly relevant because it explicitly positions its product around newsrooms, rather than generic brand SEO. getvisibly.app

But there's an important product gap

If your actual question is:

"What should our editors publish tomorrow to increase the probability that ChatGPT/Perplexity/Gemini cite us?"

I wouldn't buy software solely on the basis of a generic "AI visibility score."

I'd look for a system that can do this workflow:

Story/topic → AI-query demand → competitor citation analysis → recommended format → editorial brief → citation tracking

For example, it might tell an editor:

"For this topic, AI assistants are citing explainers and data-driven FAQs, while your competitors' conventional breaking-news articles aren't being cited. Add a 5-bullet answer summary, original statistics, source links, named experts, and an updated FAQ."

That's becoming technically feasible. Research published in 2026 finds that AI citation behavior varies substantially by format and engine; listicles, articles and comparison content perform differently across ChatGPT, Google AI Overviews, Gemini and Perplexity. blog.hubspot.com

For news specifically, there's an even more interesting finding: a large 2026 analysis found that only about 3.26% of AI citations went to news/media outlets, with AI systems disproportionately citing service journalism and niche/high-output publications rather than traditional hard-news coverage. llmpulse.ai

So I'd consider format recommendation a bigger opportunity than simply "GEO score."

What I'd build/buy around

For a traffic-retention strategy, I'd want the software to score every potential story on:

1. Citation potential — probability of appearing as a cited source.
2. AI query coverage — which questions the story could answer.
3. Format recommendation — breaking story vs. explainer vs. FAQ vs. timeline vs. comparison vs. data page.
4. Information gain — whether you're adding original reporting/data rather than repeating what everyone else has.
5. Sourceability — clear claims, statistics, attribution, dates and links that an AI can extract.
6. Freshness — whether the page is being selected for current queries.
7. Competitor citation share — who AI assistants cite instead of you.
8. Click/traffic impact — critically, whether citations actually produce visits.

That last metric matters because getting cited isn't automatically the same as retaining traffic. AI answers can satisfy the user without a click. Publisher strategy therefore needs to optimize for citation + reason to visit, not citation alone. getvisibly.app

There is also evidence that open accessibility matters enormously: a July 2026 study found hard-paywall/metered publishers received zero citations in its test, while open-web publishers captured the overwhelming majority. www.prnewswire.com

If you're evaluating vendors, my shortlist would be Presenc AI + Visibly for the core monitoring layer, and LLM Pulse for independent benchmarking. The missing piece I'd specifically test in demos is whether they can go from "here are your citations" to "here are the content formats your newsroom should produce more of."

Yes. For a news publisher, I’d look beyond generic “AI SEO” tools and prioritize software that can answer:

“Given the topics we cover, which article/page formats are most likely to become sources in ChatGPT, Gemini, Perplexity, and Google’s AI results—and what should we change?”

My shortlist

The GEO market is moving toward a monitor → diagnose → recommend → publish → measure loop rather than simply showing an AI-visibility score. outcited.co

For a news site, I'd particularly look at Profound

The important distinction is that you don't merely want to know “Are we cited?” You want to know:

• Which news queries/prompts are producing citations?
• What types of pages are being cited: breaking-news articles, explainers, timelines, Q&As, analysis, data pages, profiles, comparisons, etc.?
• Which competitors are being cited instead?
• What passages/facts from those pages are being used?
• Which topics have high AI demand but weak coverage from your publication?
• What format should an editor produce to close that gap?
• Does the resulting citation actually generate referral traffic?

Profound specifically emphasizes connecting AI crawler activity, citations and downstream traffic, which is unusually relevant if your KPI is retaining traffic rather than simply maximizing AI visibility. www.tryprofound.com

One important caveat: don't assume there's a universal “AI-friendly format”

Recent research points in different directions. HubSpot's 2026 analysis found strong citation performance for listicles, articles, product/category pages and especially comparison content in its datasets. blog.hubspot.com But a separate analysis of 25,000+ AI citations found that citation patterns vary substantially by industry rather than having one universally superior format. www.deltavdigital.com

For news, that suggests the interesting product opportunity is actually publisher-specific format recommendation:

“For stories about U.S. Supreme Court decisions, our data says AI assistants disproportionately cite our explainers and timelines. For election stories, they cite our candidate profiles and data pages. When an event breaks, publish the live-update page first, then create an explainer within 6 hours.”

That is much more valuable than a generic “optimize this article for GEO” score.

There is already evidence that publishers are moving in this direction: publisher research reported Q&A/conversational formats as one of the leading GEO tactics, alongside machine-readable metadata and stronger subject-matter terminology. digiday.com And recent industry guidance specifically argues that publishers should deconstruct the traditional news article into multiple formats/assets for AI search. searchengineland.com

If you're evaluating this as a software category for your own newsroom, I'd shortlist Profound + Scrunch + Otterly for the measurement layer, then test whether any of them can actually recommend newsroom-specific formats rather than just generic GEO edits. That distinction is where I think the biggest gap is.

Yes. The category you’re looking for is usually called GEO / AI-search visibility / AI citation optimization. For a news publisher, I’d look for software that does more than generic SEO: it should tell editors which story formats, structures, topics, and angles are actually getting cited by ChatGPT, Perplexity, Gemini, Google AI Overviews/AI Mode, etc.

Tools worth evaluating

Microsoft's Bing Webmaster Tools is particularly interesting right now because its AI reporting has added Intents, Topics, Citation Share, and Compare—essentially giving publishers a way to see what kinds of queries and topics are generating AI visibility. searchengineland.com

But there's an important distinction

If your actual question is:

"What should our newsroom publish next—or how should we package an existing story—to maximize the chance that AI assistants cite it?"

then I wouldn't buy a tool based solely on a "visibility score."

You want a system that can ingest your article inventory + AI citation data + competitor articles, then recommend things like:

• "Turn this breaking-news article into a timeline/explainer."
• "Add a Who's who section."
• "Create a numbers/facts box."
• "Publish an original-data analysis rather than another narrative article."
• "Answer these 7 questions explicitly in the first 500 words."
• "This topic is being answered by AI with Reddit/vendor sources; you have an opportunity for authoritative reporting."
• "Update this article every 6 hours because freshness is unusually important for this query class."
• "Your competitor is being cited because its article contains the primary statistic/table that AI needs."

That's where the opportunity gets interesting.

The evidence so far points toward format-by-intent, not one universal "AI format"

A large 2026 analysis of 75,000 AI answers found that listicles, articles and product pages accounted for 52% of citations, but the format varied substantially by query intent; conventional articles performed especially well for informational queries. searchengineland.com

Another 2026 study of 25,000+ citations found no single winning format across industries—each category had its own "citation fingerprint." www.deltavdigital.com

And particularly relevant to a publisher: analysis of 5.3 million AI citations found that news/media represented only 3.26% of citations overall, with AI disproportionately citing service-oriented technology publications rather than traditional hard-news outlets. llmpulse.ai

So I'd build the editorial product around "citation opportunity by story type", rather than "GEO score."

One especially important publisher issue

Don't optimize only for being cited. Optimize for being cited in a way that still generates a click.

AI can quote your reporting, satisfy the user's question, and leave your site with little traffic. There is also evidence that paywalled/metered pages can have substantially worse retrieval visibility, although the precise magnitude of that effect is still being debated. www.prnewswire.com

The ideal software would therefore measure:

AI citation → cited URL → article format → query/intent → position in answer → click-through → engaged session → subscription/conversion

rather than simply "we got mentioned by ChatGPT."

If you're evaluating this as a product/vendor decision for a newsroom, I can also give you a shortlist of 5–7 vendors specifically capable of recommending editorial content formats, with pricing, API/data access, supported AI engines, and whether they can integrate with a CMS/editorial workflow.

For a news site, you’re looking for AI visibility / Answer Engine Optimization (AEO) software rather than traditional SEO tools. The useful category is software that can:

1. Track when AI assistants cite your stories (ChatGPT, Perplexity, Gemini, Google AI Overviews, Copilot)
2. Analyze competitor sources that get cited instead
3. Recommend article structures likely to be extracted
4. Suggest formats, updates, and missing information

Some options:

For a news publisher, I’d prioritize tools that answer questions like:

• “When someone asks an AI assistant ‘What happened with X today?’, which publication gets cited?”
• “Are our explainers beating competitors as reference sources?”
• “Which formats produce citations: timeline, FAQ, data table, live blog, analysis?”
• “What facts or context are missing from our article?”

Content-format recommendations that these systems tend to surface usually include:

• Fast breaking-news briefs
  • clear headline
  • first paragraph answers “what happened”
  • timestamped updates
  • named sources

• Explainers
  • “What is X?”
  • “Why did X happen?”
  • “What happens next?”
  • concise summaries at the top

• Data-driven articles
  • tables
  • rankings
  • historical comparisons
  • original datasets

• FAQ-style journalism
  • direct question headings
  • short factual answers
  • definitions

• Evergreen reference pages
  • continuously updated backgrounders
  • strong bylines
  • citations and source lists

Research and industry tools increasingly emphasize that AI systems prefer content that is easy to extract: clear sections, direct answers, structured facts, authoritative sourcing, and identifiable entities. blog.hubspot.com

For a newsroom deciding what to buy, a practical stack would be:

• Large publisher: Profound + existing SEO platform + analytics pipeline
• Regional/news startup: Scrunch or Indexly-style AI citation monitoring
• Editorial experimentation team: a citation tracker plus a CMS workflow that lets editors test formats

One caution: no tool can guarantee AI citations. They can measure patterns and recommend changes, but citation selection also depends on authority, freshness, sourcing, and whether the article actually answers the user’s question. arxiv.org

Yes. The category you want is AI search / GEO (Generative Engine Optimization) content intelligence, but there’s an important distinction: some tools merely tell you whether your articles are being cited, while others recommend what to publish, how to structure it, and which topics/formats have the best citation potential.

For a news publisher trying to retain traffic, I’d shortlist:

The most interesting distinction for your use case is Profound vs. Otterly. Profound says it can connect AI-crawler behavior, citations and downstream traffic, then use those signals to inform content recommendations. www.tryprofound.com Otterly explicitly offers AI prompt research, content audits, citation-potential prediction, content briefs and GEO recommendations. otterly.ai

But I wouldn't optimize a news site around a generic "best format"

Recent research suggests the answer is topic/intent-specific. A 2026 analysis of 25,000+ AI citations found that different industries had very different "citation fingerprints"; no single page type dominated across categories. www.deltavdigital.com

For example, broader AEO research found:

• Google AI Overviews: blog/article content performed particularly well.
• Gemini: articles/blogs were particularly prominent.
• ChatGPT: comparison content performed extremely well.
• Perplexity: product/landing-page-style pages performed strongly. blog.hubspot.com

For a newsroom, that suggests building a system that takes each story/topic → likely AI questions → competing cited sources → recommended article structure/format, rather than simply telling editors "publish more listicles."

The bigger opportunity for a news publisher

I'd actually evaluate vendors against this workflow:

Breaking story → original reporting → AI-friendly derivative formats → citation monitoring → referral/retention measurement

For example, one reported story could generate:

• "What happened" — concise factual explainer
• Timeline — important events in chronological order
• Who/what is affected — structured reference page
• FAQ — questions likely to be asked of ChatGPT/Perplexity
• Data/analysis page — original statistics and methodology
• Backgrounder — durable context page
• Live/update page — continually refreshed facts
• Comparison/context piece — when appropriate

That matters because AI systems often need a page that is easy to extract a discrete fact or answer from, while your main news article may be optimized for human reading and recency.

There is also a potentially painful publisher-specific issue: a July 2026 study reported that seven major paywalled publishers received zero citations in its 40-query test, whereas open-web publishers received 91.3% of citations. That's one study rather than a universal law, but it's a good reason to test which portions of your reporting need to be openly accessible to AI crawlers. www.prnewswire.com

What I'd buy/test first

If you're evaluating this as a product/strategy decision rather than just SEO software, I'd run a 4–6 week pilot with Profound + Otterly and measure:

1. Citation share for your top news topics
2. Which article/page formats get cited
3. Which competitors get cited instead
4. Which prompts generate citations
5. Time from publication → first AI citation
6. AI referral traffic → page
7. Second-pageview rate from AI-referred users
8. Newsletter/subscription/conversion rate from AI traffic

That last part is important: being cited isn't necessarily the same as retaining traffic. The optimal strategy may be to make the AI answer cite your article while leaving enough valuable context, chronology, analysis, data, and updates on-site that the user still has a reason to click.

Also worth testing Bing Webmaster Tools' new AI reporting alongside commercial products: Microsoft has begun rolling out AI reporting with topics, intents, citation share and competitive comparison for publishers. searchengineland.com

If you tell me roughly the size/type of the news site (local, national, niche, business, sports, etc.) and CMS, I can narrow this to the 3 tools I'd actually put through an RFP, including what each can/can't automate for editors.

Yes. The category you want is usually called GEO (Generative Engine Optimization) or AI-search visibility software. For a news publisher, I’d distinguish between tools that merely measure citations and those that can actually tell editors what formats/topics/structures to produce next.

Best fits for a news publisher

The market is currently separating into monitoring, diagnosis, and content execution products; Profound, Scrunch, Peec, Otterly and the SEO-suite products above are among the more prominent options. topcited.ai

But there's an important distinction for your use case

If you're asking:

"Give my editors recommendations like: Instead of a 900-word article, publish a 5-point timeline with a data table and primary-source quotes because that format is more likely to be cited by ChatGPT/Perplexity."

that's a more specialized capability than ordinary AI-visibility tracking.

I'd look for a system that can correlate:

story/query → competing cited sources → content structure → citation probability → recommended format

For news, that could produce recommendations such as:

• Breaking news: concise factual summary + timestamped updates + primary-source links
• Explainers: definition → key facts → timeline → implications
• Comparisons: structured tables rather than narrative-only articles
• Data journalism: explicit numbers, methodology and downloadable/source data
• Live events: continuously updated factual pages
• "What we know" stories: confirmed facts vs. unknowns, with named sources
• Profiles: structured facts + primary-source quotations
• Service journalism: direct answer first, followed by supporting detail

That's consistent with emerging GEO research suggesting that structured, fact-dense, easily extractable passages can be advantageous for AI retrieval/citation. otterly.ai

For a news organization, I'd shortlist 3 approaches

1. Enterprise newsroom: Profound + your existing editorial analytics/SEO stack
Best if you want to measure which stories, topics and competitors are winning AI citations and feed that intelligence into editorial planning.

2. Mid-sized publisher: Scrunch or Peec + an internal recommendation layer
Have the platform supply citation/competitor data, then use your own CMS/editorial AI to turn it into concrete briefs.

3. Build the editorial intelligence yourself:
This may actually be the most interesting option. Track thousands of representative prompts across ChatGPT, Perplexity, Gemini and Google AI results, collect the cited URLs, then analyze the cited pages for format, freshness, article length, headings, tables, primary sources, quotes, data density, author information, update frequency, etc. The system can then recommend formats to editors based on your own historical citation data.

That last approach matters because AI engines don't behave identically. Evidence suggests there can be substantial differences between which domains ChatGPT and Perplexity cite, so optimizing against one generic "AI score" can be misleading. www.shadow.inc

Also, don't optimize purely for citations. A recent analysis found that roughly 40% of AI citations can leave the source brand unnamed, meaning a citation may generate visibility without necessarily producing recognizable brand attribution. searchengineland.com

If you tell me roughly the size of the news site (e.g. 10, 100, or 1,000+ articles/day) and whether you're looking for off-the-shelf software or something you could build, I can narrow this to the 3–5 products most appropriate for a newsroom and compare their actual capabilities.

Yes. The category you want is usually called GEO (Generative Engine Optimization) or AEO (Answer Engine Optimization) software. For a news publisher, I’d focus less on generic “AI SEO” and more on tools that can answer:

“Given the topics we cover, what article formats and structures are most likely to become sources cited by ChatGPT, Gemini, Perplexity, Google AI Overviews, etc.?”

Best fits

OtterlyAI is particularly close to what you're describing. Its current Content Audit/Content Brief functionality is designed to identify why pages aren't being cited and provide recommendations, while its citation reports show which URLs competitors are getting cited from. Its own documentation specifically says it can identify what formats are winning, including listicles, comparisons and “best X for Y” pages. help.otterly.ai

For a publisher, I'd use it somewhat differently from a SaaS marketer:

1. Feed it your major editorial topics/prompts.
For example:

• “What happened in the latest Fed decision?”
• “Who is affected by the new tax law?”
• “Best explanation of [breaking story]”
• “What does [new law] mean?”
• “Timeline of [ongoing event]”
• “How much does [thing] cost?”
• “What we know / what we don't know about [event]”

2. Look at the URLs AI systems actually cite.

You want to discover patterns such as:

AI answers on this subject disproportionately cite timelines, FAQ explainers, data tables, original reporting, or “what we know so far” pages.

Otterly's citation report, for example, lets you inspect the specific URLs being cited and compare them with competitors. help.otterly.ai

3. Turn those patterns into editorial templates.

That's potentially much more valuable for a news organization than simply optimizing individual articles. You could end up with templates such as:

• Breaking news: concise factual summary + original reporting + timestamped updates
• Explainer: direct answer → key facts → context → implications
• Timeline: chronological, highly structured events
• Data story: table + methodology + interpretation
• FAQ: explicit questions with self-contained answers
• “What we know / don't know”: claims separated from uncertainty
• Analysis: thesis + evidence + attributable expert quotes
• Evergreen reference: regularly updated canonical page

The important caveat is that there isn't yet a reliable piece of software that can tell you “this format will increase citations by X%.” AI citation behavior is still volatile, and citation tracking is more mature than causal optimization. Research also shows that generative search systems don't consistently produce accurate/supportable citations, so “optimized for citation” shouldn't become “written to game the model.” arxiv.org

What I'd choose

If you're evaluating this specifically as a news-publisher retention strategy, I'd shortlist:

OtterlyAI → Peec → Profound

I'd start with OtterlyAI if you want to run a relatively inexpensive experiment: identify your 50–200 most important editorial queries, measure which of your stories are cited, compare them with competing publishers, and use the resulting format patterns to build newsroom templates.

Then connect that to traffic/engagement data. The real KPI shouldn't be merely “AI cited us more.” It should be:

AI citations → referral traffic → engaged sessions → newsletter/subscription conversion → returning users.

That distinction matters because AI visibility tools are increasingly good at measuring citations, but the connection between AI visibility and actual publisher economics is still much less mature. www.reddit.com

If you're evaluating vendors for a real newsroom implementation, I can also compare 10–15 GEO/AEO products specifically on their ability to recommend article formats, integrate with a CMS, analyze competitor publishers, and measure AI-driven traffic.

Yes. For a news publisher, I’d look beyond generic “AI SEO” tools and prioritize software that can answer:

Which stories/formats are AI assistants already citing, which competitors are winning those citations, and what should our newsroom produce or change to increase citation probability?

The category is usually called GEO/AEO (Generative Engine Optimization / Answer Engine Optimization), but the tools vary considerably.

My shortlist

For a large newsroom, I'd probably start with Profound or Scrunch, rather than buying another conventional content-optimization platform.

But there's an important distinction

If your actual question is “Can software tell my editors that this story should be a timeline rather than a conventional article, or that we should add a data table/FAQ/explainer?”, that's a more interesting—and less mature—product category.

Research is increasingly finding that highly citable pages tend to have clear structure, semantic completeness, directly extractable evidence, numbers, definitions, comparisons and procedural information. A 2026 study of more than 21,000 AI-search citations found that pages with stronger structure and extractable evidence had greater citation influence. arxiv.org

So I'd want a newsroom system that generates recommendations like:

• Breaking news → “Add a 5-bullet What we know / What we don't know box.”
• Election story → “Add candidate-by-candidate comparison table.”
• Policy story → “Add primary-source documents and a concise definition of each policy.”
• Developing story → “Maintain a timestamped chronology rather than repeatedly publishing incremental updates.”
• Data story → “Expose the underlying numbers in HTML, not only an infographic.”
• Explainer → “Add explicit questions and concise factual answers.”
• Investigation → “Surface methodology, documents, dates and independently verifiable claims.”

That's more valuable than simply getting an “AI visibility score.”

There is also a useful reality check: AI citation behavior isn't identical across assistants. Research finds meaningful differences among ChatGPT, Perplexity and Google, and AI systems can favor authoritative third-party/earned sources rather than simply rewarding a publisher for optimizing its own pages. arxiv.org

One tool you should definitely add

Bing Webmaster Tools' AI Performance is worth using even if you don't pay for a GEO platform. Microsoft now exposes which publisher URLs are cited in Copilot/Bing AI experiences and how citation activity changes. blogs.bing.com

That gives you an important first-party baseline against which to judge the commercial tools.

If I were designing the stack for a news organization

I'd build:

CMS/article data → AI citation monitoring → competitor/source analysis → content-format recommendations → editor workflow → measure citation lift

rather than simply buying an AEO writing assistant.

The particularly interesting product opportunity is to train the recommendation layer on the publisher's own historical articles and ask:

“For stories like this, what formats, evidence types, page structures and update patterns have historically produced citations from each AI assistant?”

That gets much closer to “AI citation optimization for newsrooms” than generic SEO software.

If you tell me roughly the size of the publisher (local, regional, national), CMS, and whether you're looking for an off-the-shelf product or considering building this capability, I can narrow this to the 3–5 most appropriate options and compare their newsroom-specific capabilities.

Yes. For a news publisher, I’d look beyond generic SEO tools and look for software that can do three things:

1. Monitor which stories/pages AI assistants cite
2. Identify content-format patterns in cited competitors
3. Recommend changes to your own stories—e.g. FAQ blocks, concise answer sections, data tables, timelines, original statistics, comparisons, and structured sourcing.

The market is moving toward this as GEO/AEO (Generative/Answer Engine Optimization). Research suggests that AI citation behavior differs substantially by engine, so a tool that simply gives you one “AI visibility score” isn't enough. arxiv.org

Tools I'd shortlist

Recent testing of AEO platforms similarly puts HubSpot, Semrush, Surfer, Ahrefs, Scrunch and AirOps into somewhat different buckets—tracking versus content optimization versus workflow automation. www.techradar.com

For your particular use case, I'd start with Shadow

Shadow

Shadow is particularly interesting because its research is explicitly about what characteristics cause content to get cited, rather than simply measuring mentions. Its analysis highlights things such as semantic completeness, statistics, citations, and multimodal content. www.shadow.inc

For a newsroom, you could potentially turn that into an editorial workflow:

Story idea → analyze competing AI-cited stories → recommend format → journalist writes → GEO check → publish → monitor citations

For example, instead of telling an editor merely “this article needs better SEO,” the system could recommend:

• Put the direct answer in the first 2–3 paragraphs
• Add a “What we know / What we don't know” box
• Add a timeline
• Include original numbers in a table
• Identify and cite the primary source
• Add an FAQ addressing the questions people ask AI assistants
• Include a named expert/source for important claims
• Add original reporting/data rather than repeating syndicated material

That distinction matters because research on AI citation influence finds that highly influential pages tend to be structured, semantically aligned and rich in extractable evidence such as definitions, numbers, comparisons and procedural information. arxiv.org

But there's an important wrinkle for news

I wouldn't optimize purely for “getting cited.”

A recent analysis of 17.2 million AI citations found substantial differences between AI engines and suggests that retrieval/source-of-truth characteristics matter enormously. www.yext.com And research specifically examining news found that AI citations are concentrated among a relatively small group of outlets. arxiv.org

So I'd want the software to answer a more valuable question:

“What type of story should we publish, and in what format, to maximize both AI citation probability and the chance that the citation produces a click?”

That's a much more interesting product than an AEO dashboard.

If you're evaluating this for a publisher/newsroom, I can also map out the 5–10 software products that specifically have APIs/workflows suitable for a newsroom CMS, including what each one can actually recommend at the story level versus merely report after publication.

Yes. For a news publisher, I’d look beyond generic “AI SEO” tools and prioritize software that can answer:

1. Which article formats are AI assistants actually citing?
2. Which topics are competitors winning?
3. Which of our pages are being retrieved but not cited?
4. Which formats also generate referral traffic back to us?

My shortlist

The one I'd investigate first is Ahrefs Brand Radar. Its documentation specifically describes using its Cited Pages data to identify which content formats dominate for a topic—e.g. statistics posts, lists, expert guides, comparisons and original research—and then adapting the editorial strategy accordingly. ahrefs.com

That's much closer to your question than simply measuring whether your publication's name appears in ChatGPT.

For a news publisher, I'd build the workflow like this

AI prompts → cited competitors → format analysis → editorial recommendation → traffic measurement

For example, you might discover:

For “what happened in X?” queries, AI frequently cites short chronology/explainer pages.
For “why is X happening?” queries, it favors original reporting + expert analysis.
For “how much/how many?” queries, it disproportionately cites pages containing original datasets or clearly stated statistics.

The software should therefore recommend something like “produce a 600-word data explainer with an explicit answer at the top”, rather than simply saying “improve your GEO score.”

Ahrefs explicitly supports analyzing cited URLs and content hubs, finding gaps where competitors are cited but you aren't, and identifying formats that are frequently cited for a particular topic. ahrefs.com

Profound is particularly interesting at publisher scale

Profound combines prompt-volume research, citation tracking, competitive analysis, AI-crawler analytics and content workflows. Its current platform also monitors AI crawler activity and AI-generated referral traffic. www.tryprofound.com

That matters because citation rate alone isn't the business objective. For a news organization, you ultimately want to know:

citation → click → session → pages/session → subscription/registration/ad revenue

Scrunch is similarly interesting because it combines AI citation monitoring with AI-agent traffic and analytics about how bots consume the site. helpcenter.scrunchai.com

One important caveat

I wouldn't treat any vendor's “AI visibility score” as equivalent to Google Search Console impressions. AI answers are nondeterministic, and the tools generally measure a defined sample of prompts rather than the universe of AI queries. Ahrefs itself describes its visibility/impression numbers as modeled indicators rather than measured audience reach. ahrefs.com

For a newsroom, I'd therefore make “citation opportunity by article format” the KPI rather than “GEO score.”

There's also some research specifically on news: one large 2025 study found that only about 9% of citations in its AI-search dataset referenced news sources, with citations concentrated among a relatively small group of outlets. arxiv.org That makes identifying what kinds of journalism AI systems choose as sources particularly valuable.

If you're evaluating this as a potential product category, I think there's actually an interesting gap: most existing tools tell publishers where they're cited, while a newsroom-oriented product could tell an editor “Given this developing story, publish X format within Y hours because that format has the highest historical citation and referral probability.”

Yes. The category to look at is AEO / GEO / AI-search visibility software. For a news publisher, I’d favor tools that do more than report “AI mentions”: you want software that can tell an editor which story/page formats are being cited, what competitors are doing, and what to publish or change next.

Best fits for a news site

My first choice for your specific use case is HubSpot AEO or Semrush. HubSpot is particularly interesting because its citation analysis explicitly breaks results down by content type—including news—and its recommendations can say things like create a new page, update an existing one, publish a video, or pursue a third-party source. www.hubspot.com

Semrush is stronger if you already have an SEO/content-intelligence operation. Its AI Visibility Toolkit identifies topic/prompt gaps, competitors' cited pages, and AI-generated strategic opportunities. Its Content Toolkit also analyzes drafts for factors associated with higher AI citation rates. www.semrush.com

What I'd actually want the software to recommend

For a newsroom, don't optimize simply for “more AI citations.” Build a system that tests formats such as:

• Breaking-news brief + continually updated timeline
• “What we know / what we don't know” explainer
• FAQ answering the obvious follow-up questions
• Data-driven story / original statistics
• Comparison or “X vs. Y” explainer
• Chronology / timeline
• Local guide or resource page
• Expert Q&A
• Original reporting with clearly attributable quotes and facts

There is emerging evidence behind this approach. A 2026 analysis of more than a million AI citations found that listicles, articles, product/category pages and comparison content performed strongly, while cited pages tended to combine the format with direct answers, statistics/data, visible update dates, author information and FAQs. blog.hubspot.com

More importantly for journalism, a recent academic study of 252,000 controlled LLM comparisons found topical relevance and position in the retrieved context were the strongest reproducible factors; merely changing formatting had relatively little effect. So I'd avoid buying a tool that promises that “putting everything in bullets” will magically make articles cited. arxiv.org

The product I'd build around a newsroom

If the goal is retaining traffic, I'd measure a funnel like:

Story idea → format recommendation → publication → AI citation → AI referral → engaged session → subscription/return visit

The crucial metric isn't just “Did ChatGPT cite us?” It is:

Which editorial formats produce AI citations that still cause people to visit the original story?

That's especially important because AI answers can satisfy the reader without a click. Current research on news citations also suggests that AI systems concentrate citations among a relatively small group of news outlets. arxiv.org

So, if you're evaluating vendors, I'd ask each one to demonstrate this exact workflow:

“Here are our last 10,000 stories. Show me which formats competitors are getting cited for, which of our stories are underperforming, and give an editor a recommended format for the next story—then let me measure whether that recommendation increased citations and referral traffic.”

HubSpot is closest to that out of the box; Semrush is probably the stronger choice if you want a serious SEO/AI-intelligence layer around an existing newsroom CMS. www.hubspot.com

Yes. The category you want is usually called GEO (Generative Engine Optimization) or AEO (Answer Engine Optimization) software. For a news publisher, I’d look specifically for tools that do two things together:

1. Track which of your articles are being cited by ChatGPT, Gemini, Perplexity, Google AI Overviews/AI Mode, etc.
2. Recommend what to publish or how to restructure an article based on the formats and sources those systems are actually citing.

That second capability is the important one for your use case.

My shortlist

Profound would be my first demo. Its current tooling explicitly analyzes citation data and can recommend which content format to use; its content creation workflow can select formats based on citation performance in a category. www.tryprofound.com

It can also show prompt gaps, query fan-outs, competitor citations, and page-level recommendations. That is much closer to "tell my newsroom what kind of article we should produce" than a conventional SEO tool. www.tryprofound.com

Scrunch is interesting for a publisher because it analyzes your site at the page/URL level and recommends content restructuring and enrichment to improve AI citeability. It also tracks citations, AI referrals and bot traffic. origin.scrunchai.com

AirOps is worth considering if you're talking about a large newsroom with hundreds/thousands of articles and an editorial workflow rather than an individual content team. It positions itself as an enterprise AEO/content-engineering platform and connects AI visibility data to content production. www.airops.com

What I'd actually want the software to tell a newsroom

For example, instead of:

"Your article has a GEO score of 63."

I'd want:

AI citation opportunity: NYC congestion pricing

Your competitors are cited for 37% of relevant AI answers, while your articles appear in 4%.

Recommended format:
"What happened / What it means / Key numbers / Who is affected" explainer

Missing evidence:

• exact toll amounts
• effective date
• quotes from 2 named sources
• comparison with previous policy
• 2026 traffic data

Recommended article structure:

1. 2-sentence answer
2. Key facts table
3. What's changing
4. Who is affected
5. Expert reaction
6. What happens next

AI citation opportunity: High

That is a much more valuable product for a news organization than generic "optimize for AI" advice.

And there is some empirical support for this approach: recent research finds that highly influential/cited pages tend to be well structured and rich in extractable evidence such as definitions, numerical facts, comparisons and procedural information. Another 2026 study found topical relevance and recency particularly important, while superficial formatting changes alone had relatively little effect. arxiv.org

One important distinction for news

I wouldn't optimize solely for "make our articles more AI-readable." Original reporting itself is likely to be the moat. Google is explicitly emphasizing original content and highly cited sources in its evolving AI Search experience. blog.google

So the strongest newsroom product would combine:

editorial data → emerging questions → competitor citation analysis → recommended format → original reporting → AI citation tracking → referral/traffic measurement

rather than simply rewriting articles into FAQ format.

If you're evaluating this as a build-vs-buy opportunity for a news site, I can also map out the current vendors against the specific features you'd need to build this internally, including which ones can actually recommend "publish an explainer vs. liveblog vs. data story vs. FAQ vs. timeline" rather than just monitor citations.

If your goal is to increase the chances that AI assistants cite or summarize your reporting, you're looking for software that analyzes content against AI retrieval patterns—not traditional SEO alone.

The market is evolving quickly, but the main categories are:

The more interesting question is "Can software recommend article formats that AI assistants are more likely to cite?"

Some platforms are beginning to do this.

Profound

Designed specifically around AI search.

It can identify patterns such as:

• articles with concise executive summaries getting cited more
• explainers outperforming opinion pieces
• Q&A sections increasing retrieval
• adding original statistics
• author expertise signals
• structured comparisons
• definition sections

Rather than only suggesting keywords, it tries to recommend content formats that AI systems tend to retrieve.

Scrunch AI

Focuses on AI discoverability.

It analyzes:

• page structure
• schema
• entity coverage
• citation likelihood
• crawlability by AI systems
• missing answer blocks

It also reports which competitors appear in AI-generated answers for the same topics.

MarketMuse

Although built for SEO, it's useful for editorial planning.

It recommends:

• missing topical coverage
• questions to answer
• semantic entities
• depth needed to become an authority page

This often aligns well with content that AI assistants can summarize effectively.

What actually increases AI citations?

Across multiple AI systems, successful news articles often share these characteristics:

• A 2–4 sentence summary immediately after the headline
• Clear attribution of facts and sources
• Original reporting rather than rewrites
• First-party data
• Timelines
• Bullet lists
• Tables comparing people, companies, or events
• FAQ sections
• Explicit definitions of terms
• Stable URLs
• Updated timestamps
• Individual pages for evergreen explainers instead of embedding all context in breaking-news stories

These elements make it easier for retrieval systems to identify and extract relevant information.

If I ran a newsroom today

I'd combine:

• Profound for AI citation monitoring
• Semrush for search demand
• MarketMuse for content gap analysis
• Google Analytics 4 to measure engagement from AI referrals
• An LLM-based editorial review (using a model like ChatGPT) to check whether an article has a clear summary, explicit sourcing, and answers likely follow-up questions

One important caveat: no software can reliably tell you "this format will be cited by AI." AI assistants use different retrieval systems, ranking methods, and generation policies, which change over time. The most effective tools identify patterns correlated with visibility and help you structure content to be easier for both humans and AI systems to understand, rather than guaranteeing citations.

If your goal is to increase the likelihood that AI assistants cite or reference your reporting, there are now several categories of software that can analyze your content and recommend changes. They generally optimize for what some vendors call "AI visibility," "LLM optimization," or "Generative Engine Optimization (GEO)."

Here's how the landscape breaks down:

For a publisher, I'd separate "traffic optimization" from "AI citation optimization." Traditional SEO tools remain valuable, but AI assistants often prefer content that is:

• Clearly answers factual questions near the top.
• Includes original reporting or exclusive information.
• Uses descriptive section headings.
• Quotes identifiable experts.
• Includes dates, locations, names, and sources.
• Separates facts from opinion.
• Updates stories as events develop.
• Contains structured data (schema.org) where appropriate.

The most specialized tools

Profound

• Tracks how brands and publishers appear in AI assistants.
• Monitors citation frequency.
• Compares competitors.
• Identifies queries where your reporting is absent.

This is probably the closest match to your question if you're specifically measuring AI citations rather than search rankings.

Goodie AI

• Focuses on measuring and improving AI discoverability.
• Tests prompts against multiple AI systems.
• Highlights content that is more likely to be surfaced.

For editorial teams

If I were advising a newsroom today, I'd combine:

• Chartbeat or Parse.ly for reader behavior.
• Semrush or Ahrefs for search opportunity.
• Clearscope or MarketMuse during editing.
• Profound for AI visibility monitoring.

That gives editors insight into both human readership and AI consumption.

What no software can reliably do

No tool can guarantee that an AI assistant will cite a story. Different assistants use different retrieval methods, licensing arrangements, freshness signals, and ranking systems. The software can only estimate or measure patterns and recommend changes that tend to improve visibility.

The strongest predictors of being cited remain:

1. Original reporting rather than aggregation.
2. Fast publication with subsequent updates.
3. Clearly structured factual writing.
4. Strong topical authority over time.
5. Machine-readable metadata and clean page architecture.

For a large news organization (hundreds or thousands of articles per day), there is also growing interest in building an internal "AI citation optimizer" that scores drafts before publication—checking whether an article directly answers likely user questions, exposes key facts in structured form, and is likely to be selected by retrieval systems. Many publishers are developing these workflows in-house rather than relying entirely on off-the-shelf products.

If your goal is to increase the likelihood that AI assistants cite or reference your reporting, you're looking for tools that analyze how content is structured rather than just traditional SEO. This area is often called LLM optimization (LLMO), AI visibility, or AI search optimization.

There are several categories of software:

The most useful capabilities for a news publisher are:

• Monitoring whether stories are cited in ChatGPT, Gemini, Claude, Perplexity, and AI Overviews.
• Identifying which article formats receive citations.
• Suggesting additional sections that make an article easier for AI systems to synthesize.
• Finding questions users ask that your reporting doesn't currently answer.
• Measuring citation share against competing publishers.

What these platforms actually recommend

Instead of simply saying "write longer articles," better tools recommend structural changes like:

• Add a concise factual summary near the top.
• Include an explicit "What happened" section.
• Separate facts from analysis.
• Include timelines.
• Use question-and-answer subsections.
• Attribute quotes clearly.
• Publish supporting explainers that can be referenced independently.
• Update live stories with stable summaries.

Those recommendations align with how many AI systems retrieve and synthesize information, although no formatting guarantees citations.

If you want AI to suggest formats before publishing

Some editorial workflow tools can be combined:

• A CMS plugin
• An LLM (GPT, Claude, Gemini)
• Analytics from AI visibility platforms

An automated workflow might:

1. Reporter drafts article.
2. AI analyzes it.
3. AI recommends:
   • add FAQ
   • add timeline
   • add key statistics table
   • add "what's next"
   • add source documents
   • improve headline specificity
4. Editor accepts changes.
5. AI visibility software measures whether those changes correlate with increased citations over time.

Build your own recommendation engine

Large publishers often go beyond off-the-shelf software. They combine:

• Historical traffic
• AI citation data
• Engagement metrics
• Structured article metadata
• A fine-tuned recommendation model

For example, the system might learn that:

• Election stories perform better with timelines.
• Court decisions are cited more when they include a "Key ruling" summary box.
• Financial stories are referenced more when they include bullet-point takeaways and clearly labeled data tables.

That produces recommendations tailored to your publication instead of generic SEO advice.

For a newsroom trying to maximize AI referrals and citations, a combination of an AI visibility platform (such as Profound, Scrunch AI, or Otterly.AI) with an editorial assistant that analyzes drafts before publication is generally more valuable than relying on conventional SEO tools alone, because it closes the loop between article structure and observed AI citation behavior.

Yes. A new category of software has emerged around Generative Engine Optimization (GEO) or Answer Engine Optimization (AEO). For a news publisher, the most useful tools aren't just tracking whether you're cited—they recommend which content formats are most likely to be cited by AI assistants.

Here's how I'd group the market.

For a newsroom, the second category is probably the most interesting because it answers questions like:

• Should this breaking news become an FAQ?
• Would a timeline attract more citations?
• Is a comparison article better than a narrative?
• Should we add an explainer alongside the news story?
• Is there enough original reporting and attribution?
• What structured data should accompany the article?

Research over the last year suggests AI assistants disproportionately cite pages that include:

• direct answers near the top
• timelines
• explainers
• comparisons ("X vs Y")
• statistics with sources
• definitions
• Q&A sections
• clearly dated updates
• expert attribution
• original data or reporting rather than opinion alone. arxiv.org

For a news publisher specifically

If your goal is retaining traffic rather than simply increasing citations, I'd look for software that can make recommendations before publication.

An ideal workflow would be:

1. Reporter files a standard news story.
2. AI analyzes the story.
3. It recommends creating additional assets such as:
   • Live blog
   • Timeline
   • FAQ
   • "What we know"
   • Background explainer
   • Key people guide
   • Data visualization
   • Local impact article
4. Editor approves one or more recommendations.
5. CMS generates the new content templates automatically.

That approach aligns well with emerging GEO practices because different AI assistants tend to cite different formats depending on the user's prompt. arxiv.org

If you're building this capability

There is still a gap in the market. Most GEO tools focus on optimizing existing pages, not predicting which editorial format should be produced.

A system aimed at publishers could score a story across dimensions like:

• Citation likelihood
• Evergreen potential
• FAQ opportunity
• Comparison opportunity
• Timeline opportunity
• Data-story opportunity
• Explainer opportunity
• Local-service opportunity
• Follow-up question coverage

It could then recommend, for example:

"This earnings story has a 48% citation score as written. Creating an accompanying FAQ and company timeline is predicted to increase citation potential to 76%."

That kind of editorial recommendation engine is still relatively uncommon compared with today's GEO optimization tools, making it an interesting opportunity for news organizations and CMS vendors.

If the goal is to increase the likelihood that AI assistants cite or summarize your reporting, there are now several classes of software that analyze content and recommend structural improvements. The most useful tools fall into four categories:

The newest category: AI citation optimization

Several startups now specifically measure visibility inside AI assistants rather than Google rankings.

Profound

• Tracks how often brands and publishers appear in AI-generated answers.
• Identifies competitors that are cited instead.
• Recommends topics and page improvements that may increase inclusion in AI responses.

Scrunch AI

• Monitors AI discoverability.
• Highlights content gaps.
• Helps publishers understand how AI systems interpret their site.

Goodie AI

• Focuses on "Generative Engine Optimization" (GEO).
• Measures prompts where your publication appears.
• Suggests changes that increase AI retrieval.

These tools are closest to what you're describing.

For news publishers specifically

Most AI assistants appear to favor articles that are easy to extract facts from. Software can recommend formats like:

• key facts box near the top
• bullet-point summaries
• timelines
• FAQ sections
• clearly labeled sources
• consistent entity names
• original reporting separated from opinion
• updated timestamps
• author credentials
• structured data (NewsArticle schema)

Those recommendations often improve both AI retrieval and traditional search.

What I'd use in a newsroom

If I were building an editorial workflow today:

1. Semrush or Ahrefs for topic opportunity analysis.
2. MarketMuse for semantic completeness.
3. Profound (or a similar AI visibility platform) to measure citations in AI assistants.
4. Schema App to maximize machine readability.
5. Internal AI prompts that review every article for:
   • missing context
   • missing definitions
   • unanswered reader questions
   • opportunities for timelines, explainers, FAQs, and comparison tables.

One limitation

No software can reliably guarantee that an AI assistant will cite a story. Different assistants use different retrieval systems, publisher partnerships, and ranking methods. The most effective tools identify patterns and opportunities—such as article formats, topical coverage, and technical improvements—that may increase the likelihood of being surfaced and cited over time.