ChatGPT answers, unedited

If your goal is creating content outlines that conversational AI systems are more likely to quote/cite, you’re looking for tools in the emerging GEO (Generative Engine Optimization) / AEO (Answer Engine Optimization) category. The best choice depends on whether you need brief generation, content optimization, or AI citation tracking. Rankable SitePoint

Best tools for AI-quotable content outlines

1. Frase — best for content briefs and outlines

Best for: SEO writers, content teams, agencies

Why it fits:

• Builds topic outlines from search results and competitor analysis
• Identifies questions, headings, and missing subtopics
• Helps structure content around direct answers (useful for AI extraction)

Use it when you want:

• “Create a comprehensive outline for a page AI assistants can quote”
• FAQ sections
• Comparison tables
• Question-based headings

2. Surfer SEO — best if you already have an SEO workflow

Best for: Teams optimizing existing articles

Strengths:

• Content scoring
• SERP-based outline recommendations
• Entity/topic coverage suggestions

It is more SEO-first than GEO-first, but strong topical coverage often overlaps with what AI systems need: clear structure, completeness, and authoritative signals.

3. AirOps — best for scaling AI-focused content production

Best for: Large content operations

Useful for:

• Programmatic content workflows
• Brief generation
• AI-assisted drafts
• Editorial pipelines

Better fit for companies producing hundreds of pages rather than individual articles. SitePoint

4. Semrush — best if you need research + AI visibility tracking

Best for: Marketing teams already using SEO platforms

Useful features:

• Topic research
• Keyword/entity analysis
• AI visibility monitoring (depending on plan/features)

Some newer GEO tools focus specifically on tracking whether brands appear in AI answers rather than just rankings. SitePoint Quattr

5. Profound / Scrunch AI — best for measuring citations

Best for: Enterprises

These are less about generating outlines and more about answering:

• “Is ChatGPT mentioning us?”
• “Which prompts surface competitors?”
• “Which pages are being cited?”

That feedback can then inform new content briefs. SitePoint

If I were building an AI-citation-focused workflow

I’d combine:

1. Research/outlining - Frase or Surfer
2. Drafting - A strong LLM with your own editorial rules
3. GEO validation - Profound, Scrunch AI, or another citation tracker
4. Human credibility layer - Original data
5. Expert quotes
6. Specific examples
7. Clear definitions

AI systems tend to favor pages that are easy to extract from: direct answers near the top, descriptive headings, concise definitions, structured lists, and evidence-backed claims. Rankable GeoCopy

My shortlist by use case

• Solo creator: Frase + ChatGPT/Claude
• SEO/content marketer: Surfer + Semrush
• Agency: Frase + AirOps
• Enterprise brand: Semrush/Ahrefs + Profound/Scrunch AI
• Pure GEO experimentation: a dedicated GEO platform that tracks AI citations and recommends content changes Geosoftwarerankings Quattr

A practical test: pick a tool that can generate an outline containing (1) the exact user question, (2) a 40–80 word answer block, (3) supporting sections, (4) FAQs, and (5) sources/entities to reference. That format is closer to what answer engines can reliably extract. GeoCopy

If your goal is specifically “create content outlines that conversational AI systems are likely to quote/cite”, I’d separate the problem into outline generation and AI-visibility measurement. There isn't yet a single tool that reliably does both.

My picks

Frase is probably the closest match to what you're asking for. It combines SERP research, questions, content briefs and outlines, and current comparisons specifically identify it as particularly strong for brief generation. Conbersa gtm.help

But there's an important distinction: traditional SEO outline tools aren't actually optimizing directly for ChatGPT/Claude/Gemini citations. They mostly infer what makes content useful from Google search results. Dedicated AI-visibility tools instead measure whether AI systems actually mention/cite your content. Cakewalk xSeek

The workflow I'd use

1. Frase → generate the initial outline

Have it research:

• questions people ask
• competing pages
• semantic concepts/entities
• headings
• missing subtopics

2. Manually transform the outline for LLM quotability

This is the crucial step. I'd structure each important section around a self-contained answer unit:

Question/claim → direct answer → evidence → qualification/context → source

For example, rather than:

H2: Benefits of CRM software
H3: Improved productivity
H3: Better customer relationships

I'd want something more like:

H2: What are the main benefits of CRM software?
Opening answer: “CRM software primarily improves customer-data organization, sales follow-up, and visibility into customer interactions.”

H3: How does CRM software improve sales productivity?
Direct 2–3 sentence answer.
Supporting evidence/data.
Caveat about when the benefit doesn't apply.

That creates lots of small, independently understandable passages that an answer engine can potentially retrieve.

3. Add an AI-visibility tracker

This is where dedicated GEO/AEO software becomes useful. Current AI-visibility products can monitor whether systems such as ChatGPT, Gemini and Perplexity cite or recommend particular sources. xSeek HokAI

The important metric isn't merely “does my page rank?” but:

“When someone asks an AI a question that my page should answer, does the AI retrieve and cite my page?”

If I were buying today

For a solo marketer/small content team:

Frase + ChatGPT/Claude + an AI-visibility tracker

For a large content operation:

MarketMuse + Surfer/Clearscope + dedicated AI-visibility monitoring

MarketMuse is particularly useful when you need to decide what content to create across an entire topic, rather than merely generating the outline for one article. Cakewalk

And I wouldn't chase a hypothetical “GEO score.” AI citation behavior is still volatile—the recent decline in Reddit's share of ChatGPT citations is a good illustration of how quickly source-selection patterns can change. Axios

If you tell me what kind of content you're producing (B2B SaaS, affiliate, ecommerce, thought leadership, etc.), I can recommend a specific stack and give you the exact outline template I'd use to maximize the chance of being quoted by ChatGPT/Claude/Gemini.

If your goal is specifically “create content outlines that maximize the chance of being extracted, quoted, or cited by ChatGPT/Perplexity/Gemini/AI Overviews,” I’d separate the tooling into outline generation and AI-visibility measurement. Those are increasingly different jobs. xSeek AuditAE

My shortlist

Current comparisons consistently put Frase, Surfer, Clearscope and MarketMuse in the content-optimization category, while platforms such as Profound and Otterly are more focused on measuring AI visibility and citations. Slate Tracemetry Meridian15

If I were choosing one

I'd start with Frase.

The important thing isn't merely asking an LLM to produce an outline. You want a system that can turn the search landscape into a structured, answer-oriented brief: questions people ask, subtopics competitors cover, entities/concepts that need to be addressed, and opportunities to provide concise factual answers. Frase is one of the tools currently positioned around that workflow and AI-visibility optimization. Stork.AI xSeek

For example, instead of an outline like:

Introduction → Benefits → Features → Conclusion

you want something closer to:

• Direct answer: 40–60 words answering the primary question
• What is X? — precise definition
• How does X work? — 3–5 discrete steps
• X vs. Y — explicit comparison
• Key statistics — each with a named source
• When should someone use X?
• Limitations / exceptions
• Expert perspective
• FAQ questions expressed exactly as natural-language queries
• Concise takeaways that can stand alone when extracted

That answer-first, source-rich structure is increasingly being recommended for AI-search content. xSeek GeoCopy

The more sophisticated stack

If you're doing this professionally, I'd use:

Frase → write/edit → Semrush or Ahrefs → Profound/Otterly/Peec

The second half matters because you can't really optimize for being quoted without measuring whether you're actually being quoted. AI visibility platforms monitor prompts, mentions, citations, competitors and source URLs across conversational search engines. Tracemetry ToolChase

And there's an important caveat: there isn't a universal “GEO score” that guarantees citation. Citation behavior varies considerably between AI engines, and even within a single engine it can change over time. Recent reporting, for example, found substantial changes in which domains ChatGPT cites. Axios

One thing I'd not do

I wouldn't buy an expensive “AI content writer” simply because it claims to produce GEO-optimized articles.

For your particular objective, the valuable capability is:

query research → entity/topic coverage → answer extraction opportunities → outline → sourced content → citation tracking

rather than:

keyword → AI generates 2,000 words.

If you tell me what type of content you're producing (B2B SaaS, affiliate, news, ecommerce, thought leadership, etc.) and roughly how many articles/month, I can narrow this down to the 2–3 tools I'd actually pay for, including current pricing and the exact workflow I'd use.

If your goal is specifically “create content outlines that maximize the chance of being cited/quoted by ChatGPT, Perplexity, Gemini, etc.”, I’d prioritize GEO/AEO-native tools over traditional SEO outline generators.

My picks

I'd start with Frase. Its current GEO workflow explicitly scores drafts for AI search and recommends things such as putting a direct answer near the top, strengthening entity coverage, and adding fresh, quotable facts. www.frase.io

If you're building an AI-quote-optimized outline

The software should ideally help you produce an outline like:

1. Direct answer / definition
2. Short, self-contained answer to the primary question
3. Key facts and statistics
4. Comparison/table where appropriate
5. Expert explanation
6. Specific examples
7. Subquestions users actually ask AI
8. FAQ section
9. Sources / evidence
10. Clear entities and terminology

The important distinction is that traditional SEO tools optimize for ranking, whereas AEO/GEO tools increasingly optimize around the prompts AI systems answer and the sources they cite. Profound, for example, explicitly analyzes real prompts, tracks which pages receive citations, and identifies citation gaps against competitors. www.tryprofound.com

One important caveat

No software can guarantee that ChatGPT or another model will quote your page. The strongest workflow is:

AI prompt research → citation/competitor analysis → outline → human-written content → GEO optimization → citation monitoring.

For that workflow, I'd choose:

Solo/small team: Frase
Serious AEO program: Profound + Frase
Primarily SEO with some AI optimization: Surfer

If you tell me what type of content you're producing (affiliate, SaaS, ecommerce, B2B, publisher, local, etc.), I can narrow this down to the best 2–3 tools and the exact outline workflow I'd use.

If your goal is to create content outlines that are more likely to be retrieved, summarized, and quoted by conversational AI systems (ChatGPT, Gemini, Perplexity, Claude, AI Overviews), you’re looking for GEO/AEO (Generative Engine Optimization / Answer Engine Optimization) tools rather than traditional SEO outline generators. rankable.ai

My recommendations by use case:

For outline generation specifically, I’d use this workflow:

1. Research questions
   • Ahrefs / Semrush / AlsoAsked-style tools → find conversational queries.
2. Build the outline
   • Frase or Surfer → generate headings, subquestions, and coverage requirements.
3. Optimize for citation
   • Add:
     • direct-answer sections (“What is X?”)
     • short definitions
     • numbered processes
     • comparison tables
     • original data/examples
     • expert attribution
     • clear entity names

These formats are easier for models to extract and reuse. arxiv.org

4. Measure
   • Use GEO visibility tools like Profound, GeoGen, Quattr, or similar platforms to see whether AI answers actually reference your content. www.geogen.io

If I were choosing a stack today:

• Solo creator: Frase + Ahrefs
• Marketing team: Surfer + Ahrefs + a GEO tracker
• Enterprise brand: MarketMuse/enterprise SEO platform + Profound/Quattr-style AI visibility monitoring
• Primary goal = “get quoted by ChatGPT”: prioritize GEO-specific tools rather than only SEO tools. www.sitepoint.com

A key point: no software can guarantee that ChatGPT or another model will quote you. The tools mainly help you create content with the characteristics these systems tend to select: clear answers, strong structure, authoritative evidence, and easy-to-extract facts. arxiv.org

If your goal is specifically content outlines that increase the odds of being quoted/cited in ChatGPT, Perplexity, Gemini, AI Overviews, etc., I’d look at GEO/AEO tools rather than ordinary AI writing software.

My shortlist

My pick for actually generating the outline: Surfer. Its Content Editor now has AI Search Guidelines, including recommendations intended to make content more likely to be picked up/cited by AI systems, and its outline workflow incorporates missing facts, entities, and coverage opportunities. docs.surferseo.com

My pick for figuring out what to put in the outline: Profound. It analyzes which URLs AI answer engines cite for particular prompts and, importantly, its query-fan-out research shows the subsidiary searches/models use to construct an answer. That's extremely useful for designing an outline around the information an answer engine actually needs. www.tryprofound.com

Otterly.AI is a good lighter-weight alternative if you want to start from conversational prompts: it identifies prompts people may use with AI search engines, monitors the resulting answers/citations, and provides content briefs and GEO audits. otterly.ai

The workflow I'd use

Rather than asking an LLM, "give me an SEO outline," I'd do:

Profound/Otterly → identify real AI prompts → inspect cited competitors → Surfer → generate/optimize outline → write concise, independently citable sections → monitor citations.

The important distinction is that "ranking for a keyword" and "being quoted by an answer engine" aren't quite the same optimization problem. For the latter, your outline should deliberately create:

• a direct answer near the beginning
• self-contained factual claims
• clearly attributable statistics/data
• explicit definitions
• question → answer sections
• named entities and relationships
• comparison tables where appropriate
• primary/authoritative sources
• concise passages that can be extracted without surrounding context

Surfer's current AI Search Score explicitly considers facts/entities/concepts coverage and upfront intent alignment, which is very close to what you're describing. docs.surferseo.com

If you're doing this at scale for a content operation, I'd lean Profound + Surfer. If you're a solo content marketer and want one tool, Surfer is probably the most practical starting point.

If your goal is specifically “create content outlines that increase the odds a conversational model quotes/cites my content”, I’d look beyond traditional SEO brief tools.

The key distinction is that ranking optimization ≠ AI-citation optimization. Traditional tools mostly analyze SERPs, keywords, entities, and topical coverage; newer GEO/AEO tools also track whether AI systems actually mention or cite your site. slatehq.comwww.xseek.io

My shortlist

1. Frase — best if the immediate job is generating better outlines

Frase

I'd start here if your workflow is:

topic → research → questions → outline → writer

Frase is particularly strong at briefs/outlines, SERP analysis, and surfacing questions to answer. Recent comparisons consistently identify its brief-generation workflow as one of its strengths. www.conbersa.aitorontoseo.com

The limitation: it isn't primarily an AI-citation measurement platform.

2. MarketMuse — best for building the underlying topical authority

MarketMuse

Use this if you're producing a large content library and want outlines informed by:

• topical gaps
• related concepts
• content inventory
• topic clusters
• what your existing site is missing

It's particularly useful when the question isn't merely “what should this article contain?” but “what does my entire site need to establish authority on this subject?” www.conbersa.aitorontoseo.com

3. Surfer — best all-around SEO + content optimization workflow

Surfer

Good choice if you want one system covering research, outline/content planning, optimization, and drafting. Its core strength is SERP-derived content optimization rather than AI-citation research specifically. www.conbersa.aitorontoseo.com

4. An AI-visibility platform — best if “quoted by ChatGPT/Perplexity/etc.” is the actual KPI

This is the category I'd investigate most closely for your particular objective. Platforms such as Peec AI, AthenaHQ, Scrunch, xSeek, and Rankability focus more directly on monitoring AI-search visibility: which prompts produce mentions, which competitors get cited, and where you're absent. slatehq.com

For example, xSeek describes the distinction well: content optimization platforms answer “is our content good enough?”, whereas AI-visibility platforms answer “are AI engines actually saying/citing us?” slatehq.comwww.xseek.io

What I'd actually use

For a serious GEO/AEO content operation, I'd use a two-layer stack:

Research/outline layer: Frase or MarketMuse
AI-visibility layer: an AI-search visibility platform

Then make the outline itself deliberately citation-friendly:

1. Put the answer to the main question near the top.
2. Give important claims their own concise, self-contained paragraphs.
3. Use explicit definitions rather than implying them.
4. Include original data, examples, comparisons, and first-party evidence.
5. Structure sections around the questions users actually ask conversational models.
6. Make individual claims easy to extract without surrounding context.
7. Cite primary sources within your own article.
8. Avoid burying the answer under 500 words of introduction.

That last part is important: there isn't a magic “AI citation score.” You want to make your content both discoverable and extractable. AI-search optimization and conventional SEO overlap, but they're not identical. slatehq.comwww.xseek.io

If I had to pick just one for your stated use case: Frase for generating the outlines, then manually modify the resulting brief around atomic, evidence-backed answer blocks. If you're publishing at scale, I'd add an AI-visibility tracker so you can measure whether those structural changes actually increase citations.

If you tell me what kind of content you publish (B2B SaaS, ecommerce, medical, finance, etc.) and roughly how many articles/month, I can recommend the specific stack and workflow I'd use.

If your goal is specifically “create content outlines that give ChatGPT/Claude/Perplexity/etc. clean, quotable passages”, I’d separate this from ordinary SEO content optimization.

My picks

I'd start with Frase. Its strength is the brief/outline workflow: SERP analysis, competitor coverage, question research, and answer-oriented structure. Current comparisons also consistently identify it as particularly strong for research-first content briefs. www.therankmasters.com

But there's an important caveat:

No outline generator can really guarantee that conversational models will quote your content. The optimization target is better thought of as GEO/AEO (Generative/Answer Engine Optimization) rather than simply “SEO for ChatGPT.” Current tools generally combine traditional SERP signals with question coverage, topical/entity coverage, and AI-visibility measurement. www.xseek.io

What I'd actually use

For each article, I'd build the workflow like this:

1. Frase → generate the research-backed outline

Have it identify:

• questions users ask
• competing pages
• important subtopics/entities
• missing topics
• suggested H2/H3 structure

2. Add an “AI citation layer” to the outline

This is the part I'd not leave entirely to software. For every important section, specify:

Question: What is X?
Answer: 40–70 word self-contained answer.
Evidence: specific source/data/example.
Elaboration: deeper explanation.

That creates passages that an answer engine can extract without needing five paragraphs of context.

3. Make claims unusually explicit

Instead of:

There are several factors that can affect conversion rates.

Prefer:

The three biggest factors affecting ecommerce conversion rate are product-page clarity, perceived trust, and checkout friction.

That's much more “quotable”: a model can lift the sentence and preserve its meaning.

4. Use an AI-visibility tracker separately

This is increasingly important because traditional content scores tell you whether you're covering the SERP, whereas AI-visibility platforms tell you whether models are actually mentioning/citing you. The two are different problems. www.xseek.io

If I were building the stack

Solo/small team:
Frase + ChatGPT/Claude + an AI visibility tracker

SEO/content team:
Surfer + Frase + AI visibility tracker

Enterprise/content strategy:
MarketMuse + Clearscope/Surfer + AI visibility tracking

MarketMuse is particularly interesting if you're trying to establish topical authority across an entire site, rather than merely generating a good outline for one article. honestaiguide.com

One other thing: don't optimize exclusively for “being quoted.” I'd optimize for being selected as a source. A model that cites your page is considerably more valuable than one that merely happens to reproduce a sentence from it.

If you tell me what kind of content you're producing (B2B, SaaS, affiliate, medical, local, thought leadership, etc.), I can give you a much more specific GEO/AEO outline template + software stack.

If your goal is specifically content outlines designed to increase the chance that ChatGPT, Perplexity, Gemini, Claude, etc. will quote/cite your content, I’d separate the tools into two categories:

My picks

1. Profound — best fit for your exact goal

Profound is the one I’d look at first. It is built specifically around AI search / answer-engine optimization (AEO) rather than traditional SEO. It can identify real prompts people are asking, track which sources AI systems cite, compare you against competitors, and generate optimization recommendations. www.tryprofound.comwww.tryprofound.com

Its content-optimization workflow explicitly looks at citation data, AI readability, prompts you're missing, and competitors getting cited more often, then turns those findings into content recommendations. www.tryprofound.com

2. MarketMuse — best for actually building sophisticated outlines

MarketMuse is probably stronger if what you mean by "outline" is a genuinely detailed editorial brief. Its briefs include headings, questions to answer, points of view, related topics, structure, intent, expertise, facts, and other guidance. help.marketmuse.comwww.marketmuse.com

It also analyzes hundreds to thousands of pages rather than simply copying the structure of the top few Google results. www.marketmuse.com

The important distinction

For being quoted by conversational models, I wouldn't optimize an outline merely for:

keyword → H2 → related keywords → 2,000 words

I'd want the outline to produce pages containing lots of self-contained, quotable passages:

• Direct answers to specific questions
• Clear definitions
• Explicit claims rather than vague prose
• Original data/research
• Expert attribution
• Specific numbers, dates, and comparisons
• Concise explanations that can stand alone when extracted
• FAQ/question-based sections
• Strong entity relationships and factual context
• Sources for important claims
• Distinctive insights rather than generic summaries

That's where Profound + MarketMuse is a particularly good combination: use Profound to determine what AI systems are actually citing and which prompts/citation gaps matter, then MarketMuse to turn that research into a comprehensive content brief. Profound's current platform specifically provides citation tracking and recommendations based on competitors' citation performance. www.tryprofound.com

If you only want one

One caveat: no software can guarantee that an LLM will quote your content. Citation behavior depends on the model, retrieval system, query, competing sources, freshness, authority, and the particular answer being generated. The useful optimization target is therefore increasing citation probability, not "writing content that tricks ChatGPT into quoting it."

If you tell me what type of content you're producing (e.g. B2B SaaS, affiliate reviews, medical, finance, ecommerce, thought leadership), I can also tell you exactly what an AI-citation-optimized outline should contain and which of these tools is worth paying for.

If your goal is specifically “create content outlines that increase the odds of being quoted/cited by ChatGPT, Gemini, Perplexity, Claude, etc.”, I’d choose differently than if your goal were traditional SEO.

My picks

The current tool landscape still largely splits into traditional content optimization (SERP/semantic scoring) and newer AI-visibility/GEO platforms. No single tool reliably “optimizes for being quoted by LLMs”; the better approach is to combine question research + topical coverage + answer-oriented structure + original evidence. www.xseek.iowww.aisearchrankings.com

🥇 I'd start with Frase

Frase

For your particular use case, Frase is probably the closest match because it is brief/outline-first: it analyzes search results, questions, competitors and topic coverage and turns that into an outline/brief. Recent comparisons specifically identify its research-to-outline workflow as its strongest feature. www.therankmasters.comhonestaiguide.com

But I'd modify the generated outline substantially for LLM visibility.

Instead of:

H2: Benefits of X
H2: Types of X
H2: How to choose X

I'd want the outline to produce sections more like:

What is X?
40–60 word definition that can stand alone as an answer.

What is X best used for?
Direct answer + supporting evidence.

X vs. Y: What's the difference?
Explicit comparison table.

What are the limitations of X?
Specific, independently verifiable claims.

When should you choose X instead of Y?
Decision criteria.

Frequently asked questions
Individual questions answered directly rather than buried in paragraphs.

That structure creates lots of self-contained answer units that a conversational model can potentially retrieve and quote.

The important distinction

Don't think of this as “writing for AI.”

Think of it as:

Make every important claim easy for an AI retrieval system to identify, understand, verify, and attribute.

That means your outline should explicitly request:

• Answer-first sections
• Short, self-contained explanations
• Clear definitions
• Explicit comparisons
• Specific numbers/dates where appropriate
• Original research and data
• Named experts/sources
• Citations immediately adjacent to claims
• Tables for structured comparisons
• FAQ/question-based headings
• Clear entities and relationships
• Statements that don't depend on surrounding context
• A distinction between fact, interpretation, and opinion

Traditional SEO tools don't fully enforce all of this. One recent AEO comparison makes the same point: conventional tools can help with topical coverage and SERP alignment, but don't necessarily handle direct-answer formatting, entity authority, query-to-format matching, etc. www.xseek.iowww.aisearchrankings.com

A better stack

If I were building this workflow today, I'd use:

Frase → ChatGPT/Claude → AI-visibility tracker

1. Frase

Find questions, competing coverage and build the initial outline.

2. ChatGPT or Claude

Turn that into a GEO-oriented editorial brief rather than blindly accepting the generated outline.

3. AI visibility platform

Test whether your existing/published content actually gets mentioned for target conversational queries. This is a different problem from optimizing the article itself; platforms such as xSeek and other AI-search trackers focus on the “are AI engines actually mentioning us?” layer. www.xseek.io

4. Surfer or Clearscope, optionally

Use these for traditional semantic/on-page optimization after the GEO structure is established. Surfer is particularly useful if you're producing content at volume. www.therankmasters.comwww.stork.ai

If you're serious about LLM citations

I'd actually make the outline itself a proprietary template, rather than relying on whatever a SaaS tool generates.

A good GEO outline should contain columns/fields such as:

Section → User question → Direct answer → Key claims → Evidence/source → Entity → Supporting data → Citation opportunity → Related questions → Internal links

That's considerably more useful for LLM visibility than a conventional:

H1 → H2 → H3 → keywords → word count

So my recommendation is:

Frase if you want one tool today.
Frase + a custom GEO briefing framework if AI citations are strategically important.
MarketMuse instead if you're managing a large site and your bigger problem is establishing topical/entity authority across hundreds of pages. www.therankmasters.comhonestaiguide.com

If you tell me what kind of content you produce (B2B SaaS, ecommerce, medical, finance, publisher, etc.), I can also give you a specific GEO/AEO outline template designed around how conversational models select and quote sources.

If your goal is specifically “create content outlines that make the finished article more likely to be extracted, cited, or quoted by ChatGPT/Perplexity/Gemini”, I’d use a GEO/AEO tool rather than a conventional SEO content brief generator.

My picks

OtterlyAI is the one I'd test first. It now explicitly offers AI prompt research, content audits, content briefs, citation analysis, and optimization recommendations across ChatGPT, Gemini, Perplexity, Copilot and Google's AI search experiences. help.otterly.ai

The important distinction is that you don't merely want an outline that ranks. You want an outline whose eventual sections contain self-contained, evidence-rich passages that a retrieval/generative system can confidently use as evidence. Recent research on GEO found that highly influential cited pages tend to be structured and rich in extractable evidence—definitions, numerical facts, comparisons and procedural steps. arxiv.org

The workflow I'd actually use

1. OtterlyAI → discover the conversational prompts

Instead of starting with:

“best project management software”

build around questions such as:

“What is the best project management software for a 20-person agency?”

“How does Asana compare with ClickUp for agencies?”

“What project management software is easiest for nontechnical teams?”

Otterly specifically supports discovering the kinds of natural-language prompts people use with AI search. otterly.ai

2. Analyze which pages/models are currently being cited

Don't just ask “what keywords should I include?” Ask:

• Which URLs are being cited?
• What claims from those URLs are being used?
• What information do the cited pages contain that competitors don't?
• Which questions aren't adequately answered?
• Which sources are repeatedly trusted?

This matters because citation selection and citation influence aren't the same thing—a page can be cited without contributing much of the final answer. arxiv.org

3. Generate the outline with ChatGPT/Claude

I'd then have an LLM turn that research into a brief where every major section has:

• Question the section answers
• 1–2 sentence answer that should appear immediately
• Supporting facts/data
• Primary/authoritative sources to cite
• Comparisons or distinctions
• Examples
• Expert commentary
• “Quotable” claims
• Internal links/entities to establish
• What competing articles fail to answer

That is considerably more useful than a generic “H2/H3 + keywords” brief.

One important caveat

There isn't a magic “make ChatGPT quote me” outline format.

Current research suggests AI search behavior varies substantially by engine, and factors such as authority, external references, structure, evidence, and semantic alignment matter. One 2025 GEO study also found a strong preference for earned/third-party sources over brand-owned content. arxiv.org

So I'd think about the objective as:

Prompt discovery → source/citation analysis → evidence-rich outline → highly extractable content → external authority → AI visibility measurement

rather than:

AI-friendly outline → automatic citations.

If you tell me what kind of content you're producing (B2B SaaS, ecommerce, healthcare, finance, publisher, etc.), I can also recommend the exact software stack + outline template I'd use for maximizing LLM citations.

If your specific goal is creating content outlines that increase the odds of being quoted/cited by ChatGPT, Gemini, Perplexity, etc., I’d prioritize tools that do more than traditional SEO keyword clustering.

My shortlist

1. MarketMuse — my pick for outline quality

MarketMuse's briefs go substantially beyond "here are 10 headings." They incorporate intent, related topics, questions to answer, points of view, information gaps, facts, and content-type-specific structures. It also analyzes hundreds to thousands of pages rather than simply mimicking the top few Google results. www.marketmuse.com

That's particularly relevant because recent research on AI citation behavior suggests that highly cited pages tend to be semantically aligned, well structured, and rich in extractable evidence—definitions, numerical facts, comparisons, and procedural information. arxiv.org

MarketMuse

2. Surfer — best practical option

Surfer is attractive if you actually want to go from keyword → outline → draft → optimization in one workflow. Its Research & Create Outline feature uses competitor data to generate outlines, and its current Content Editor includes both an SEO Score and an AI Search Score. docs.surferseo.com

Surfer

But there's an important distinction

I wouldn't optimize an outline simply for "being quoted by AI."

The more useful objective is:

Make every important section contain a concise, authoritative, independently useful answer that an answer engine can retrieve, understand, and attribute.

Research on competitive GEO suggests topical relevance and position are major factors in which source gets cited, while explicit facts such as prices and recent timestamps can also help. arxiv.org

So I'd want your outline to specify things like:

• Question the section answers
• One-sentence answer immediately under each H2/H3
• Definitions of important concepts
• Specific facts/numbers
• Comparisons and distinctions
• Original data or expert observations
• Sources/evidence supporting factual claims
• "Who/what/when/where/why" context
• Updated/fresh information
• Clear tables where comparisons are useful
• A short answer that can stand alone if extracted by an LLM

What I'd actually use

If you're building a serious content operation, I'd use:

MarketMuse/Surfer → research → custom ChatGPT/Claude outline → human expert input → draft → AI visibility testing.

The custom LLM step is important. Generic SEO software wasn't originally designed around the question "What passage from this article would an answer engine want to quote?"

And I'd measure the final result against actual prompts in ChatGPT, Gemini, Perplexity, etc., rather than trusting an "AI optimization score." The research is still young and citation behavior varies significantly between engines. arxiv.org

If you tell me your niche (e.g. SaaS, healthcare, finance, ecommerce, legal, B2B), I can also recommend the best specific GEO/AEO tool stack and an outline template designed specifically to maximize AI citations.

If your specific goal is content outlines that increase the odds of being quoted/cited by ChatGPT, Gemini, Perplexity, and other conversational search systems, I’d separate the problem into outline generation and AI-visibility measurement.

My picks

1. Surfer — my first choice for your use case.
Its current Content Editor outline system explicitly incorporates AI/brand/SEO context and produces shorter, more natural, “citation-ready” briefs. surferseo.com

Surfer

The important distinction is that I wouldn't use Surfer's score as a proxy for “will ChatGPT cite this?” Instead, I'd use it to make sure the outline has strong search-intent coverage, entities, questions, and structure.

2. Frase — probably the best if outlines are your primary deliverable.
It's particularly useful for turning SERP/question research into content briefs. Current comparisons also put it toward the lower-cost end of the serious content-optimization tools. honestaiguide.com

Frase

3. MarketMuse — use this if you're building an entire topical authority strategy.
MarketMuse analyzes your existing content inventory, identifies topic clusters and gaps, and builds content plans around your site's existing authority. That's valuable because AI citation isn't just about optimizing one article; being a credible source across a topic matters. www.marketmuse.com

MarketMuse

But there's an important catch

I wouldn't buy an “AEO/GEO outline generator” expecting it to reliably produce citation-worthy content.

The tools above are largely still descended from SEO content optimization: SERP analysis, semantic coverage, questions, structure, topical authority, etc. AI-visibility platforms are a somewhat different category—they measure whether models actually mention/cite your brand or pages across prompts. Current tool comparisons distinguish these two functions explicitly. www.xseek.io

For what you're trying to accomplish, I'd use a stack like:

Market/competitor research → Surfer or Frase → Claude/ChatGPT for the actual outline → AI-visibility tracker → human editorial review

And I'd make the outline itself contain things that are particularly useful to retrieval/generation systems:

• Direct-answer sections for important questions
• Clear H2/H3 question structures
• Short, self-contained factual passages
• Explicit definitions and distinctions
• Original statistics, research, examples, or frameworks
• Named entities and relationships
• Tables/comparisons where appropriate
• Claims tied to specific authoritative sources
• Minimal fluff before the answer
• Sections that can stand alone when extracted from the surrounding article

In other words, don't optimize merely for “what keywords should I include?” Optimize for “what passages could an AI confidently extract as an answer to a user's question?”

If you tell me what kind of content you're producing (e.g. SaaS, healthcare, finance, ecommerce, B2B), I can recommend the best specific tool + an AI-citation-optimized outline template for that niche.

If you mean content outlines designed so AI assistants (ChatGPT, Gemini, Claude, Perplexity, etc.) are more likely to quote or cite your content, you’re looking for a mix of AEO/GEO (Answer Engine Optimization) tools + content research tools. The field is still developing, and no tool can guarantee citations, but the strongest workflows combine topic discovery, structure optimization, and AI visibility tracking. www.conductor.com

My shortlist:

For generating the outline itself, I’d use this stack:

1. Research layer
   • Ahrefs / Semrush → discover high-value questions, competitors, and existing sources.
2. Outline layer
   • MarketMuse or Surfer → create a structured brief.
3. AI-quotability layer
   • Add:
     • a clear definition near the top
     • short factual statements
     • numbered processes
     • comparison tables
     • original data/examples
     • explicit answers to likely questions
4. Measurement layer
   • Profound, HubSpot AEO, or similar tools → see whether AI systems actually surface your content. www.conductor.comwww.hubspot.com

If your main goal is “I want ChatGPT/Claude/Gemini to quote my articles”, I would prioritize:

1. MarketMuse (best outline intelligence)
2. Ahrefs (best research foundation)
3. Profound (best AI visibility feedback loop)
4. Surfer (best practical writer workflow)

One caveat: optimizing only for AI extraction can produce shallow “AI bait” content. Recent research suggests that being cited depends heavily on relevance, evidence, structure, and whether the content actually provides useful information—not just formatting tricks. arxiv.org

If your goal is specifically “create content outlines that maximize the chance of being retrieved, cited, and used in answers by ChatGPT/Perplexity/Gemini”, I would not choose a traditional SEO content optimizer alone.

The strongest current workflow is GEO/AEO research + an outline generator + citation/visibility testing. Research published in 2026 suggests that highly influential sources tend to be structured, semantically aligned, and rich in extractable evidence—definitions, numbers, comparisons, and procedural steps. arxiv.orgwww.shadow.inc

My shortlist

Current comparisons put Frase, Surfer, Clearscope, etc. primarily in the content-scoring/optimization category, while newer GEO platforms focus on monitoring which prompts and AI engines actually mention/cite you. www.xseek.iowww.shadow.inc

If I were building your workflow

I'd use:

Ahrefs/Semrush → Frase → ChatGPT → AI-visibility tracker

The important distinction is that I wouldn't ask the software merely:

“What sections should this article have?”

I'd generate a brief designed around citation units, such as:

• Direct-answer paragraphs that can be lifted into an AI response
• Clear definitions
• Specific claims backed by primary sources
• Original statistics/data
• Comparisons and decision tables
• “X vs Y” distinctions
• Step-by-step procedures
• Concise answers to likely follow-up questions
• Explicit entities, terminology, and relationships
• Expert opinions/first-party evidence
• Sections answering different formulations of the same question

That's much closer to what the emerging GEO research is measuring than simply maximizing an SEO content score. One 2026 study, for example, distinguishes between a page being selected as a citation and actually having its information absorbed into the generated answer. arxiv.org

One caveat: there isn't yet a magic “AI citation score.” The GEO tooling market is still fragmented, and research finds substantial variation between engines and queries. arxiv.orgwww.shadow.inc

If you tell me what kind of content you're creating (B2B, SaaS, affiliate, medical, ecommerce, etc.), I can give you a specific software stack + exact outline template designed for AI citation.

If your specific goal is “create content outlines that maximize the chance a conversational AI quotes/cites my page”, I’d prioritize content-intelligence/brief tools plus an AEO/GEO measurement tool, rather than a generic AI writer.

My picks

Why MarketMuse is my first choice: its briefs aren't simply keyword-driven outlines. It analyzes topical coverage, questions, competitive gaps, search intent, related topics, claims, and content structure. Its briefs can specifically generate different structures for comparisons, FAQs, how-tos, guides, reviews, etc. www.marketmuse.com

That's valuable for conversational-model visibility because recent research suggests that topical relevance, position/structure, explicit factual information, recency, completeness, and trust cues can affect which source gets cited. arxiv.org

But there's an important distinction

Don't optimize solely for “AI-friendly formatting.” There's no reliable magic outline that makes ChatGPT quote you.

What you actually want is an outline that causes the finished article to contain lots of high-confidence, extractable answer units, such as:

Question → direct answer → supporting evidence → specific example/data → qualification

For example, instead of:

H2: Benefits of CRM Software

I'd want an outline closer to:

H2: What are the main benefits of CRM software?

• Give a 40–60 word direct answer.
• Quantify the benefits where credible data exists.
• Explain the three most important mechanisms.
• Distinguish benefits for SMBs vs. enterprise teams.
• Cite primary research for each important statistic.

H2: Does CRM software increase sales?

• Direct yes/no answer.
• What the evidence actually shows.
• Conditions under which it does/doesn't.
• Specific numbers and sources.

H2: Which CRM features have the biggest impact?

• Feature → outcome → evidence table.

That produces much more citation-ready material than a conventional SEO outline.

The stack I'd use

If I were building a serious content operation around this:

MarketMuse → outline/content brief
↓
Human/AI writes evidence-rich answer blocks
↓
Ahrefs Brand Radar / another AEO tracker → see which pages/models actually cite you
↓
Update the outline based on observed citations

Ahrefs' current Brand Radar is particularly interesting because it measures AI visibility across multiple engines and identifies URLs being cited, rather than treating traditional Google rankings as a proxy for everything. www.techradar.com

One caveat: AEO/GEO is still an emerging discipline, and claims that a particular formatting trick guarantees ChatGPT citations are overstated. Research is increasingly finding that relevance and useful evidence matter more than superficial formatting changes. arxiv.org

If you tell me what kind of content you're creating (e.g. B2B SaaS, affiliate, medical, finance, ecommerce, local business), I can recommend the best tool and give you the exact outline template/prompt I'd use to maximize AI citation probability.

If your goal is to create content that's more likely to be surfaced, summarized, or cited by conversational AI systems, the focus should be less on "AI SEO" tools and more on software that helps you produce well-structured, authoritative, clearly organized content. Different AI systems use different retrieval and synthesis methods, and there's no tool that can guarantee a model will quote or cite your content.

Here are the categories I'd recommend.

My preferred workflow

If I were building content specifically to maximize its usefulness to conversational models, I'd combine:

1. AlsoAsked for question discovery.
2. MarketMuse for comprehensive topic coverage.
3. InLinks for entity relationships.
4. An LLM (such as ChatGPT) to reorganize everything into a logical outline.

That combination tends to produce outlines that are:

• organized around user questions
• rich in factual entities
• comprehensive without unnecessary repetition
• easy for retrieval systems to extract from

Structure that conversational models tend to use well

Rather than long, narrative articles, structure outlines like this:

H1

What is X?

Why does X matter?

Key facts
- Fact
- Fact
- Fact

How X works
Step 1
Step 2
Step 3

Common misconceptions

Comparison table

Frequently asked questions

Sources

Content organized into discrete sections, tables, definitions, and concise factual statements is generally easier for both search engines and retrieval-augmented AI systems to parse than large blocks of prose.

Emerging GEO tools

A newer category called Generative Engine Optimization (GEO) has appeared. Some tools in this space aim to measure how brands appear in AI-generated responses rather than just search rankings. Examples include:

• Profound
• Goodie
• Peec AI
• Otterly.AI

These are useful for monitoring visibility in AI-generated answers, but they're generally complements to a solid content strategy rather than replacements for good research and writing.

If you're publishing at scale (50+ articles per month), I'd prioritize MarketMuse + InLinks + AlsoAsked. For a smaller team or solo creator, Frase + AlsoAsked + ChatGPT offers a strong balance of capability and cost.

If your goal is specifically to generate content outlines that are more likely to be cited or quoted by conversational AI systems (sometimes called GEO, Generative Engine Optimization, or AEO, Answer Engine Optimization), I'd separate tools into three categories:

That said, the software is only part of the equation. Recent GEO research suggests that the pages most often incorporated into AI-generated answers tend to share characteristics such as:

• Answer-first introductions
• Clear definitions
• Strong heading hierarchy
• Comparison tables
• Step-by-step procedures
• Original statistics or verifiable facts
• FAQ sections
• Well-defined entities (people, products, companies, concepts)
• Concise, self-contained sections that can stand alone when quoted arxiv.org

What I'd actually use

For a solo creator or small team:

• ChatGPT (or Claude) to brainstorm the topic map.
• GrackerAI to generate a GEO-focused outline.
• OptimizeGEO or Semrush AI Visibility Toolkit to monitor whether content is actually appearing in AI answers over time. gracker.ai

For agencies or larger publishers:

• OptimizeGEO
• UltraScout AI
• Semrush AI Visibility Toolkit

These platforms combine prompt monitoring, competitive analysis, and content recommendations rather than only generating outlines. www.optimizegeo.ai

One important point: there isn't a reliable way to "optimize for being quoted by ChatGPT" specifically. Different conversational AI systems retrieve, rank, and synthesize information differently, and citation behavior changes over time. The most durable strategy is to produce content that is accurate, well-structured, authoritative, and directly answers common user questions, rather than trying to target any one model.

If your goal is specifically to create content that is more likely to be surfaced or cited by conversational AI systems, you're looking for tools that help with entity coverage, topical completeness, semantic structure, and question-answer organization, rather than traditional SEO keyword optimization alone.

Here's how I'd categorize the current landscape:

If your goal is AI citation optimization rather than SEO

Traditional SEO tools are only part of the picture. Conversational models often favor content that is:

• Organized into clearly labeled sections
• Defines concepts explicitly
• Answers questions directly before elaborating
• Covers related entities and terminology
• Includes original data, examples, or expertise
• Uses consistent headings and internal structure

For that, I would combine:

1. MarketMuse for topical completeness.
2. Frase for question discovery.
3. A large language model (like ChatGPT) to restructure the outline into an AI-friendly format.

For example, instead of:

Benefits of Electric Vehicles

use:

What are the benefits of electric vehicles?

Short answer

Detailed explanation

Comparison with gasoline cars

Common misconceptions

Frequently asked questions

That structure is often easier for conversational systems to synthesize because each section addresses a discrete information need.

Emerging AI-focused tools

Some newer platforms are beginning to optimize specifically for AI search and conversational discovery:

• entity["company","Profound",""] — monitors brand visibility across AI assistants.
• entity["company","Goodie",""] (AI visibility platform) — analyzes how brands appear in AI-generated responses.
• entity["company","Scrunch AI",""] — focuses on measuring and improving AI search presence.
• entity["company","Peec AI",""] — tracks AI visibility and citations across conversational platforms.

These are primarily measurement and optimization platforms rather than outline generators, but they can inform what content is being surfaced.

A workflow that works well

A practical workflow is:

1. Research entity clusters (MarketMuse or Ahrefs).
2. Collect user questions (Frase).
3. Generate an outline with ChatGPT or another LLM.
4. Expand each heading into concise, directly answerable sections.
5. Add examples, comparisons, definitions, and FAQs.
6. Review with a semantic optimization tool before publishing.

This approach aligns the content with both human readers and the kinds of structured information conversational models tend to rely on.

If you're producing content at scale (dozens or hundreds of articles), there are also programmatic approaches that combine knowledge graphs, entity extraction, and LLM prompting to generate outlines optimized for comprehensive topic coverage rather than just keyword density.

If your goal is to create content that is more likely to be surfaced, summarized, or cited by conversational AI systems, the most useful tools are those that help you build well-structured, authoritative, semantically organized content rather than tools that promise "AI citation optimization." There isn't a reliable tool that can guarantee a model will quote or cite your content.

Here's how I'd group the options by use case:

The best tools for outline creation

1. ChatGPT

Best for creating outlines that answer questions comprehensively.

A good prompt might be:

Create an outline that fully answers the question "How does X work?" Include definitions, prerequisites, comparisons, edge cases, FAQs, misconceptions, examples, and references to related concepts.

This tends to produce outlines that are useful because they cover the kinds of information conversational models often synthesize.

2. MarketMuse

Excellent if you're publishing authoritative articles.

It analyzes:

• missing subtopics
• semantic coverage
• content depth
• topical authority

Instead of simply suggesting keywords, it recommends concepts that are often discussed together.

3. InLinks

Probably the strongest tool if your focus is entity optimization.

It builds outlines around:

• entities
• relationships
• topical clusters
• internal linking
• schema

This aligns well with how modern search and retrieval systems understand topics.

4. Clearscope

Great for editorial teams.

It identifies:

• concepts competitors cover
• important terminology
• missing sections

It's less about keywords than comprehensive topical coverage.

5. AlsoAsked

One of the simplest yet most effective research tools.

It expands Google's People Also Ask into a question tree, making it easy to build outlines that naturally answer related user questions.

Example:

What is retrieval augmented generation?

├── How does RAG work?
├── Why use RAG?
├── What are vector databases?
├── What is chunking?
├── What is embedding?
└── How is RAG different from fine tuning?

That structure maps naturally to a comprehensive article.

What tends to work well for conversational models

Across many AI systems, content is generally easier to use when it has:

• Clear H2/H3 hierarchy
• Direct answers near the beginning of each section
• Precise definitions
• Tables for comparisons
• FAQs
• Examples
• Original data or research
• Citations to primary sources
• Consistent terminology
• Structured data where appropriate

Rather than optimizing for a specific model, focus on making the content accurate, well-organized, and easy to extract information from.

A workflow that works well

1. Use AlsoAsked to collect real user questions.
2. Use Ahrefs or Semrush to identify additional related topics and search intent.
3. Use ChatGPT or Claude to draft a comprehensive outline.
4. Refine the outline with MarketMuse or InLinks to improve topical and entity coverage.
5. Publish with clear headings, structured data where appropriate, and links to authoritative sources.

This combination helps produce content that is useful for readers and is also easier for search and AI systems to understand and summarize.

If your goal is specifically to create content that is more likely to be surfaced, summarized, or cited by conversational AI systems, there isn't a tool that can guarantee "being quoted." Models don't maintain a fixed citation index, and different systems retrieve information in different ways.

Instead, the best software depends on which part of the workflow you want to optimize.

For content intended to perform well with conversational models, I would prioritize this stack:

1. Ahrefs for discovering the questions people actually ask.
2. Keyword Insights for clustering those questions into coherent topics.
3. MarketMuse or Clearscope to identify missing concepts.
4. InLinks to ensure the content is rich in well-connected entities.

The software matters less than the outline format. Outlines that tend to work well for AI retrieval and summarization generally have these characteristics:

• A clear definition immediately after the heading.
• One topic per section.
• Explicit answers to likely follow-up questions.
• Tables comparing alternatives.
• Definitions of terminology before using it.
• Statistics with attributed sources.
• FAQ sections using natural-language questions.
• Consistent terminology throughout the document.
• Strong use of identifiable entities (people, organizations, products, standards, places).
• Short, self-contained paragraphs that can stand alone when excerpted.

A useful template looks like this:

# Topic

## What is it?
Definition in 2–3 sentences.

## Why it matters
- Point
- Point

## How it works
Step 1
Step 2
Step 3

## Key concepts
Concept A
Concept B
Concept C

## Comparison
| Option | Best for | Limitations |

## Common misconceptions

## Frequently asked questions

## References

If you're publishing at scale, another category worth considering is knowledge graph and entity management rather than traditional SEO. Tools like InLinks and schema-focused workflows often produce content that's easier for search engines and AI retrieval systems to understand because they emphasize entities and relationships rather than just keywords.

If your objective is specifically optimizing for AI visibility (sometimes called GEO, Generative Engine Optimization, or LLMO, Large Language Model Optimization), I can also recommend newer tools built around AI citation analysis rather than traditional SEO.