ChatGPT answers, unedited

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.