ChatGPT answers, unedited

Yes. What you’re describing is an AI documentation observability/optimization tool: it should tell you which AI agents are accessing your docs, what they retrieve, where answers fail, and what content should change.

A few current options:

• Mintlify — AI traffic analytics showing agents, pages, queries, and drop-offs, plus AI-readable Markdown, llms.txt, and MCP. Mintlify
• GitBook — tracks AI-agent visits/pages/queries and offers AI Insights for content gaps. GitBook GitBook
• Kapa — particularly interesting if you already have a docs stack. It adds an AI retrieval layer and analytics around questions/gaps, without requiring you to migrate your docs. GitBook Kapa
• ReadMe — useful for API documentation, with AI-assisted audits/linting and MCP/LLM-oriented delivery. GitBook

If your specific requirement is: “Show me which docs ChatGPT/Claude/Cursor/etc. actually use, identify missing/poorly retrieved information, and give my technical writers concrete recommendations”, Kapa + your existing docs platform is worth investigating first; Mintlify/GitBook are more compelling if you’re also considering changing your documentation platform.

The market is moving toward exactly this workflow: AI traffic → retrieval/query analytics → content gaps → recommended doc improvements → measurement of whether retrieval improves. GitBook

Yes. What you’re describing is essentially AI visibility / GEO analytics for technical documentation—but there are two different signals worth separating:

1. Which docs AI systems actually fetch/read
2. Which docs AI systems actually cite or use in answers

For a technical-docs company, I’d look at these:

• learn.microsoft.com — particularly interesting because its Citation dashboard shows which pages are cited in AI-generated answers, which queries ground those answers, and which pages are performing well or poorly. Microsoft Learn
• documentation.ai — more documentation-specific. Its analytics can show AI-agent traffic, which pages agents read, AI-assistant questions, search failures, and content gaps. Documentation.AI Documentation.AI
• citations.io — monitors ChatGPT, Gemini, Perplexity and Claude, capturing the actual sources cited in answers and turning that into recommendations for pages/content to improve. Citations.io
• ahoylighthouse.com — explicitly promises page-level fixes tied to AI visibility and measures the result again after changes. It covers 10+ AI assistants. Lighthouse
• pendium.ai / rankry.ai — broader GEO platforms that monitor AI mentions/citations and provide recommendations, although they're more brand/product-oriented than documentation-specific. Pendium.ai Rankry Docs

For your use case, I'd define the product differently

If you're producing API/product documentation, the killer workflow isn't simply:

"How visible is our docs site to ChatGPT?"

It's:

"When developers ask AI assistants questions about our product, which documentation pages are being retrieved/cited, which questions can't be answered, and exactly what should we change in the docs?"

That suggests a dashboard like:

One important distinction: tools such as Context7 can tell you which AI products are actually requesting/fetching your docs and which pages they request, but a fetch isn't proof that the page was cited or that it influenced the final answer. context7.com

So if you're evaluating this as a tool to buy/build, I'd prioritize one that combines server-side AI crawler telemetry + synthetic AI-question testing + citation/source analysis + page-level recommendations. That combination is much more useful for a technical-docs team than a generic "GEO score."

If you tell me what docs stack you use (Mintlify, Docusaurus, GitBook, ReadMe, custom, etc.), I can narrow this down to the 3–5 tools that actually fit your workflow, including whether they can identify individual docs pages being used by ChatGPT/Claude/Cursor/etc.

Yes. What you’re looking for is essentially AI-search / AEO analytics for technical documentation: a tool that can tell you which pages AI assistants retrieve/cite, which questions you’re visible for, and where the docs have gaps.

A few options stand out:

• usecite.ai — probably the closest match if your goal is “Which of our docs are ChatGPT/Claude/Gemini/Perplexity actually citing, and how do we improve them?” It tracks citations across those assistants and includes a Site Audit that reports page-level issues affecting retrieval and quoting. Cite AI
• citations.io — tracks AI visibility across ChatGPT, Gemini, Perplexity and Claude, including the sources cited in individual answers. It also produces prioritized recommendations for pages to write or rewrite. Citations
• documentation.ai — particularly interesting if you host your docs with them. Its analytics distinguish human traffic from AI-agent traffic, show which pages AI agents read, and expose AI-assistant questions and answer usage. Documentation.AI
• bing.com — free and useful for measuring which of your URLs are cited in Microsoft Copilot/Bing AI answers, along with the grounding queries. Search - Microsoft Bing

For a technical-docs team, I'd evaluate them on 5 things

1. Citation tracking — “Which exact docs/pages are being cited?”
2. Prompt coverage — “What questions about our product are AI assistants answering, and do we appear?”
3. Competitor comparison — “Why is competitor X being cited instead of us?”
4. Content diagnosis — “What specifically should we change in this page?”
5. Before/after measurement — “Did rewriting the page actually increase AI retrieval/citations?”

The distinction between AI traffic and AI citations is important. A bot visiting your documentation doesn't necessarily mean an assistant used that page in an answer. Bing, for example, explicitly reports pages that are cited in AI-generated answers, while Documentation.AI separately reports AI-agent page views. Search - Microsoft Bing Documentation.AI

If you're producing technical docs as a service for customers, rather than just optimizing your own docs, I'd lean toward Cite AI/Citations.io as the measurement layer, and potentially build your own document-analysis workflow on top of it. The emerging research also suggests that generic “make this page more AI-friendly” advice isn't enough; documentation optimization can depend on the particular model and agent workflow. arxiv.org

If you tell me what your docs stack is (e.g. Mintlify, Docusaurus, GitBook, ReadMe, custom, etc.) and whether you want a SaaS tool or something you could build into your own product, I can narrow this down to the 2–3 best choices.

If you mean “which of our technical docs are AI assistants actually using, and how do we improve them so assistants answer better from them?”, you’re looking for a combination of AI documentation analytics + AI visibility/content optimization tools.

Some options:

For a technical documentation team, I’d evaluate tools against these capabilities:

• AI crawler analytics

• Which assistants/bots are accessing docs?
• Which pages are retrieved most often?
• Are AI systems finding the right pages?
• AI answer testing

• Ask ChatGPT/Claude/Gemini questions about your product.
• Measure whether answers cite or rely on your docs.
• Identify hallucinations or missing coverage.
• Documentation gap analysis

• “Users ask this question, but we have no page.”
• “This page is retrieved but doesn’t answer the task.”
• “The terminology differs from what users ask AI assistants.”
• Content quality scoring

• Clear task-oriented headings
• Examples and code snippets
• Structured metadata
• Machine-readable formats (for example Markdown/LLM indexes)

A practical stack many documentation teams build is:

1. Docs analytics platform → understand traffic and gaps
2. AI evaluation harness → run hundreds of realistic user questions against assistants
3. LLM-based reviewer → suggest edits to docs based on failed answers

For example, you can maintain a test set like:

“How do I authenticate with API X?”
“How do I migrate from version 2 to version 3?”
“Why am I getting error code Y?”

Then periodically test multiple assistants and rank:

• Was the answer correct?
• Did it use your docs?
• Which document was missing or unclear?

If you tell me what documentation stack you use (e.g., Markdown/GitHub, Confluence, Docusaurus, GitBook, ReadMe, Mintlify, custom site), I can suggest a more specific setup.

Yes. What you’re describing is an AI documentation visibility / GEO (Generative Engine Optimization) tool: something that can tell you which docs AI assistants actually retrieve or cite, where they fail, and what content should be improved.

A few strong options:

• gitbook.com — probably the closest match if you want analytics around AI consumption. Its current tooling can show AI-agent traffic and, through MCP analytics, the queries AI tools send to your documentation. That lets you identify things like “developers keep asking about rate limits, but our docs aren't answering it well.” GitBook GitBook
• mintlify.com — particularly good for developer/API documentation. It provides AI-agent analytics showing which agents visit, which pages they access, and searches made through MCP, alongside its assistant and documentation-maintenance agent. Mintlify Mintlify
• kapa.ai — worth considering if you already have a documentation platform and don't want to migrate. It adds an AI retrieval layer over existing docs and provides question/gap analytics. GitBook
• thally.io — focused more specifically on keeping documentation readable/useful for AI agents and detecting documentation drift. Thally

The distinction I'd make

If your requirement is specifically:

“Show me which of our pages ChatGPT/Claude/Cursor/etc. are using, what questions they're trying to answer, where our docs fail, and give our writers concrete recommendations.”

I'd look hardest at GitBook and Mintlify.

Mintlify is especially interesting for a developer-docs team because it combines agent traffic analytics + search/assistant analytics + automated documentation changes. Mintlify Mintlify

But there's an important limitation: AI-agent traffic analytics aren't the same as knowing exactly what ChatGPT or Claude answers about your company. A truly comprehensive product would periodically run a set of representative questions against multiple AI assistants, measure:

• whether your company/product is mentioned
• which documentation URLs are cited
• which specific pages are retrieved
• citation accuracy
• competitor/source substitution
• unanswered questions
• outdated or contradictory passages
• questions for which your docs should have been cited but weren't
• recommended changes to the underlying docs

That is the product category I'd call “AI visibility + documentation intelligence”, rather than merely an AI documentation platform.

If you're evaluating this because you produce technical docs and are considering buying/building such a tool, I can also give you a shortlist of 5–10 products specifically focused on measuring how ChatGPT/Claude/Gemini/Perplexity use and cite technical documentation, rather than general documentation platforms.

• whether your company/product is mentioned
• which documentation URLs are cited
• which specific pages are retrieved
• citation accuracy
• competitor/source substitution
• unanswered questions
• outdated or contradictory passages
• questions for which your docs should have been cited but weren't
• recommended changes to the underlying docs

That is the product category I'd call “AI visibility + documentation intelligence”, rather than merely an AI documentation platform.

If you're evaluating this because you produce technical docs and are considering buying/building such a tool, I can also give you a shortlist of 5–10 products specifically focused on measuring how ChatGPT/Claude/Gemini/Perplexity use and cite technical documentation, rather than general documentation platforms.

Yes. There are now tools aimed specifically at measuring how AI agents consume technical docs, rather than just traditional page views.

My shortlist

• Mintlify — probably the closest match if you want to know which AI agents are accessing which pages. Its agent analytics can identify agents, show the pages they access, and expose searches made through MCP. Mintlify
• Documentation.AI — particularly interesting for your second requirement: finding what needs improvement. It provides AI-agent traffic, AI-assistant questions, confidence levels, searches, no-result rates, and page-level feedback, so you can identify content gaps. Documentation.AI Documentation.AI
• ReadMe — has an explicit AI Discoverability audit that scores documentation against an agent-friendly spec and recommends configuration/content changes, including machine-readable llms.txt support. ReadMe
• Kapa.ai — stronger if you want to analyze the questions developers are asking AI about your product and turn unanswered/poorly answered questions into documentation improvements. Kapa Kapa
• Rankry — more focused on external AI visibility: whether ChatGPT, Claude, Gemini, Perplexity, etc. mention/cite your company and which sources they use. Rankry Docs

The distinction I'd make

If your question is:

"Which of our docs are AI assistants actually reading?"

Look at Mintlify / Documentation.AI. They give you actual agent traffic and page-level usage rather than guessing from SEO metrics. Mintlify Documentation.AI

If it's:

"What should we change in our docs so AI assistants answer questions better?"

I'd lean toward Documentation.AI or Kapa because the useful signal is not merely traffic. You want to see questions → retrieved content → answer quality → gaps, then prioritize documentation changes. Documentation.AI Kapa

If it's:

"When someone asks ChatGPT/Claude about our product, does it cite our documentation?"

That's more of an AI visibility / GEO problem, where Rankry is closer to what you're describing. Rankry Docs

One important caveat: AI-agent traffic and AI citation/visibility are different measurements. A crawler visiting a page doesn't necessarily mean an assistant used that page in an answer. Recent research also suggests traditional metrics like time-on-page and click paths become much less meaningful for AI consumption because agents can retrieve the relevant content in only one or two requests. arXiv

If you're evaluating this for a technical-docs team, I can also give you a side-by-side comparison of 5–10 tools specifically on: AI agent detection, pages used, citations, query analytics, content-gap recommendations, and API/export capabilities.

Yes. If your goal is specifically “which of our docs are AI assistants actually using, and what should we change?”, there are now tools built around that.

My shortlist

• mintlify.com — probably the closest fit if you're willing to use a docs platform. Its AI-agent analytics show which AI agents visit your docs, which pages they access, and what searches they perform. It also supports llms.txt, skill.md, MCP, and structured content delivery. Mintlify Mintlify
• gitbook.com — particularly interesting if you want query-level AI analytics and content-gap analysis. Its 2026 tooling reports AI-agent visits, pages, queries, and drop-offs. GitBook
• documentation.ai — strong if you want analytics around both human and AI consumption. It exposes AI-agent views, top pages read by agents, AI-assistant questions, search failures, and feedback, which makes it useful for deciding what documentation to improve. Documentation.AI Documentation.AI
• kapa.ai — worth looking at if you don't want to migrate your existing docs platform. It's more of an AI/retrieval layer on top of your documentation, with question and content-gap analytics. GitBook

What I'd look for in your case

The really useful system isn't just an "AI-friendly docs checker." You want a feedback loop like:

AI assistant query → docs/pages retrieved → answer → success/failure → recommended doc change

For example:

247 AI queries asked about authentication
→ 63% retrieved authentication.md
→ 28% also retrieved an outdated OAuth page
→ 19% produced no confident answer
→ Recommendation: consolidate OAuth terminology and add a concrete JWT example to authentication.md.

That is much more actionable than simply saying “your docs are optimized for LLMs.”

I'd therefore prioritize Mintlify or GitBook if you're evaluating a new documentation platform, and Kapa/Documentation.AI if you want to keep your current docs stack.

If you tell me what you currently use to publish the technical docs (GitHub/Docusaurus, ReadMe, GitBook, Confluence, custom site, etc.), I can recommend the best setup and explain exactly how to measure which docs ChatGPT/Claude/Cursor/etc. are using and turn that into an automated improvement workflow.

Yes. What you’re describing is essentially AI documentation observability + GEO/LLM optimization: identify which docs AI agents actually retrieve/cite, understand what users ask AI, then turn that into prioritized documentation improvements.

My top pick: Mintlify

Mintlify is currently one of the closest matches to your use case.

Its analytics can identify:

• Which AI agents are accessing your docs — including agent traffic that normal web analytics miss.
• Which pages AI agents access most frequently.
• What users search for and where searches fail.
• Which questions the docs' AI assistant can't answer well.
• Content gaps and high-value topics that deserve better documentation.
• Traffic/referral patterns from AI systems.

Mintlify says its agent analytics identifies AI visitors from request-level data and can show which AI platforms access your documentation and which content they request. Mintlify Mintlify

mintlify.com

Another strong option: GitBook

GitBook is particularly interesting if your goal is "make our docs more likely to be discovered, understood, and cited by ChatGPT/Claude/etc."

GitBook explicitly positions this as GEO (Generative Engine Optimization) and recommends testing AI assistants with representative questions, tracking which pages get cited, and improving pages that aren't being cited. GitBook

It also automatically provides AI-friendly Markdown, llms.txt/llms-full.txt, and an MCP server for documentation sites. GitBook

gitbook.com

The distinction I'd make

There are actually two different problems you may want to measure:

If your real goal is the full pipeline — "show me exactly which documentation pages AI assistants use, which questions they're being used for, whether the answers are correct, and what we should change" — I would not limit yourself to a documentation platform.

I'd look for an AI visibility/GEO analytics product that can sit on top of your existing docs stack, or potentially build a lightweight internal system.

A particularly useful architecture would be:

AI agent traffic → pages retrieved → AI citations → questions asked → answer quality → content gaps → prioritized doc changes

That gives your technical writers something much more actionable than conventional pageviews.

If you tell me where your docs currently live (GitHub/Markdown, ReadMe, Docusaurus, GitBook, Mintlify, custom, etc.), I can compare the best tools specifically for this workflow and identify 3–5 products that actually measure AI usage/citations rather than just traditional SEO.

Yes. If your goal is specifically “which of our technical docs are AI assistants actually using, and what should we change?”, there are now tools that get quite close to that.

My shortlist

• GitBook — probably the closest fit if you want AI-agent analytics + documentation improvement in one product. Its 2026 AI-agent analytics can show which AI tools access your docs, the queries they send, pages involved, and drop-offs. That lets you identify questions AI assistants are asking that your docs don't answer well. GitBook GitBook

gitbook.com

• Mintlify — particularly strong if you're already using Mintlify. Its Assistant provides query/source analytics, and you can export queries, responses, and cited sources to identify content gaps and high-priority pages. It also has a GEO guide specifically aimed at getting docs cited by ChatGPT, Perplexity, Google AI Overviews, etc. Mintlify Mintlify

mintlify.com

• Kapa.ai — interesting if you don't want to replace your existing docs platform. It sits on top of your existing technical content and provides question/gap analytics. It's more of an AI retrieval/assistant layer than a documentation authoring platform. GitBook Kapa

kapa.ai

• Algolia DocSearch — excellent for measuring and improving retrievability, especially if you're building your own docs/search stack. Its crawler understands Markdown structure, code snippets, tables and API references, and its Ask AI layer provides cited answers. DocSearch DocSearch Algolia

docsearch.algolia.com

The important distinction

There are actually two different things you may want to measure:

1. “Are AI assistants visiting/using our docs?”

You want agent analytics:

Claude → /authentication
ChatGPT → /webhooks
Cursor → /api-reference/users
37 AI queries asking about OAuth
14 queries with no satisfactory source

GitBook is particularly interesting here because its current analytics expose AI-tool traffic and query-level information. GitBook

2. “If an AI assistant is asked X, does it retrieve and cite the right page?”

That's more like AI search/GEO evaluation. You run a set of representative developer questions against AI systems and measure:

• Was our company/product mentioned?
• Was our documentation cited?
• Which URL was cited?
• Was the correct page cited?
• Was the answer factually correct?
• Did the model use an outdated page?
• What information was missing?
• Which competitor's documentation got cited instead?
• What changes to our docs would improve retrieval?

This second category is where I'd look beyond a normal documentation platform.

What I'd recommend for a technical-docs team

If you're evaluating tools for a serious documentation program, I'd build the workflow around this dashboard:

Then have an LLM turn those measurements into actionable documentation PRs: rewrite the intro, split a page, add a missing example, clarify terminology, add cross-links, mark version information, consolidate duplicate pages, etc.

That's more useful than simply asking an LLM to “make our docs AI-friendly.” Current guidance from Mintlify and GitBook emphasizes the same fundamentals: direct answers, clear headings, logical structure, specific technical details, metadata, and machine-readable content. Mintlify GitBook

If you tell me what you use to publish your docs (GitHub/Markdown, GitBook, Mintlify, Docusaurus, ReadMe, custom, etc.), I can recommend the best tool/stack for measuring AI citations and automatically generating improvement suggestions.

Yes. What you’re describing is usually called AI visibility / AI discoverability / GEO (Generative Engine Optimization) for documentation.

A few tools are particularly relevant:

• ahoylighthouse.com — probably the closest match to your question. It queries multiple AI assistants, tracks which pages are cited, scores pages, and gives page-level fixes. It currently says it checks 10+ AI platforms including ChatGPT, Claude, Gemini and Perplexity. Lighthouse
• citations.io — measures whether AI assistants mention/cite your brand, which sources they use, competitors, and gives prioritized recommendations for pages to write or rewrite. Citations
• rankry.ai — tracks AI visibility across ChatGPT, Claude, Gemini, Perplexity and Grok, with an AI Readiness audit and action plans. Rankry Docs
• docs.readme.com — particularly interesting if you're a documentation-platform team. It crawls your docs and scores how well they're exposed and understandable to AI agents, based on its Agent-Friendly Docs spec. ReadMe

For a technical-docs team, I'd separate the problem into 2 questions

1. "Do AI assistants actually use/cite our docs?"

Use something like Lighthouse or Citations.io. You want to see:

User question → AI answer → Was our product mentioned? → Which of our docs were cited? → Which competitor/source was cited instead?

2. "Why aren't our docs being retrieved, and what should we change?"

This is more of a documentation AI-readiness audit. Look for things like:

• unclear page titles/headings
• answers buried too deeply in prose
• missing terminology/synonyms
• fragmented information across pages
• insufficient examples
• API/reference content that's difficult to retrieve
• outdated/deprecated information
• poor internal linking
• crawler/robots/accessibility problems
• missing machine-readable documentation

Splunk's documentation guidance, for example, explicitly recommends structuring docs so AI systems can parse, understand and retrieve them. Splunk Docs

One important distinction

Tools such as Documentation.AI are somewhat different: they can show AI-agent traffic, questions, and where users are struggling with your docs, which is extremely useful for finding content gaps, but that isn't the same as measuring whether external ChatGPT/Claude/Perplexity answers cite your documentation. Documentation.AI Documentation.AI

If you're evaluating this for a technical documentation organization, I can also give you a shortlist of 5–10 tools specifically for analyzing docs' visibility to AI assistants, comparing their capabilities, pricing, API access, and whether they analyze individual URLs/pages versus the whole docs site.

One important distinction

Yes. What you’re describing is essentially AI visibility / LLM discoverability analytics for technical documentation: you want to know which docs AI systems actually retrieve/cite, which questions they fail to answer, and what changes would make your docs more useful to AI assistants.

Tools I’d look at

• Documentation.AI — probably the closest fit if your docs are hosted on their platform. Its analytics explicitly separates human traffic from AI-agent traffic, shows which pages AI agents read most, identifies which AI agents/crawlers are accessing the docs, and provides AI-assistant question/answer analytics. Documentation.AI Documentation.AI

documentation.ai

• kapa.ai — particularly interesting for developer/API documentation. It builds an AI assistant over your docs, code and support material, with cited answers. It can expose the questions developers are asking and therefore the gaps in your documentation. Kapa

kapa.ai

• AI-visibility/AEO platforms — a newer category is emerging around measuring whether ChatGPT, Claude, Google AI Overviews, Perplexity, etc. mention/cite your content. For technical docs, I'd look specifically for products that let you run a fixed set of technical questions repeatedly and track which URLs/models are cited, rather than generic SEO scores. The underlying principle is important: merely having crawlable docs doesn't tell you whether an AI actually uses them. Magrios

What I would measure

For a technical-docs team, I'd want a dashboard roughly like:

One important distinction: AI crawler traffic ≠ AI usage/citation. Anthropic, for example, has separate mechanisms for web crawling and user-directed retrieval, so seeing an AI bot hit a page doesn't prove that Claude subsequently used that page in an answer. Claude Help Center

If you're evaluating/building a tool

I'd actually define the product as:

“Google Search Console for AI-generated answers, specifically for technical documentation.”

The killer workflow would be:

Your docs → crawl/index → generate 500–5,000 realistic developer questions → ask ChatGPT/Claude/Perplexity/etc. → record retrieved/cited URLs → compare against competitors → identify gaps → generate prioritized documentation recommendations.

That is substantially more useful to a technical-writing team than simply counting AI crawlers.

If you tell me where your docs are hosted (GitBook, ReadMe, Mintlify, Docusaurus, custom, etc.), I can give you a shortlist of 5–10 tools that specifically do this, with pricing, AI platforms covered, and what each one can actually measure.

Yes. What you’re describing is generally called AI visibility / citation analytics for documentation: measuring which pages AI systems retrieve/cite, which questions they can answer, and where your docs have gaps.

Tools I’d look at

• clarity.microsoft.com — particularly interesting if your goal is “which of our docs are actually being cited by AI?” Its Citation dashboard reports cited pages, citation counts, the queries associated with them, share of authority, and AI referral traffic. Microsoft Learn
• kapa.ai — built specifically around technical documentation. It can ingest docs/code/tickets and analyze questions and answers against that knowledge. More useful if you want to understand whether your docs actually answer developers' questions, rather than just whether they're visible to AI. Kapa Kapa
• citations.io — tracks AI answers across ChatGPT, Gemini, Perplexity and Claude, including the sources those systems cite. Useful for competitive/AI-visibility monitoring. Citations.io
• documentation.ai — if you also want an AI assistant embedded in your docs. Its analytics expose AI-agent views, assistant questions, low-confidence answers, and the documentation context associated with questions. Documentation.AI Documentation.AI

For a technical-docs team, I'd prioritize this

You probably want a system that produces something like:

Page: /api/authentication/oauth
AI citations: 147
AI queries retrieving it: 32
Successful answers: 81%
Low-confidence answers: 19%
Missing topics: refresh-token rotation, PKCE errors
Recommended changes:

1. Add a "When to use PKCE" section
2. Add example for refresh-token rotation
3. Put error codes in a dedicated table
4. Add explicit version compatibility

That's more valuable than a generic "AI SEO score."

One important distinction: AI citation monitoring tells you which docs AI systems use; AI answer/evaluation tooling tells you whether those docs are sufficient and what should be improved. You ideally want both.

If you're producing technical docs at scale, I can also give you a shortlist of 5–10 tools specifically for documentation teams, comparing them on AI citation tracking, page-level analytics, content-gap detection, automated improvement suggestions, ChatGPT/Claude/Gemini coverage, and pricing.

Yes. What you’re describing is essentially “AI discoverability / AI-readability analytics for technical documentation”: identify which docs AI assistants and coding agents actually retrieve, then use that data to find gaps and improve the docs.

A few tools are particularly relevant:

• mintlify.com — probably the closest match. Its current analytics can distinguish AI-agent traffic, show which pages agents access, expose searches, and analyze assistant conversations. Its exports include unanswered queries, sources, and resolution status, which can directly surface documentation gaps. Mintlify Mintlify
• gitbook.com — has AI/search analytics showing what users and the AI assistant are asking, helping identify terminology mismatches and missing documentation. It also supports AI-oriented formats such as llms.txt, Markdown pages, and MCP. GitBook GitBook
• Your own CDN/server-log pipeline — if you don't want to migrate your docs platform, you can classify requests from AI agents by user-agent/IP and determine which pages are being retrieved. This is more engineering work but works with essentially any docs stack. Mintlify

What I'd look for in a tool

The important distinction is between “AI crawled this page” and “AI successfully used this page.” The latter is much more valuable.

Ideally, the tool should give you:

1. AI traffic by assistant/agent — ChatGPT, Claude, Cursor, coding agents, etc.
2. Pages retrieved — which docs are actually being consumed.
3. Queries/prompts — what people are asking AI about your product.
4. Unanswered questions — questions where your docs don't provide enough information.
5. Citation/source analysis — which pages are being cited in AI answers.
6. Retrieval failures — questions where relevant information exists but the AI doesn't find it.
7. Content recommendations — e.g. “Add a page explaining X,” “put this API parameter in a dedicated section,” or “use the terminology customers search for.”
8. Before/after measurement — whether a documentation change improves AI retrieval/answering.

Mintlify currently comes closest to this full feedback loop out of the box. Its assistant analytics can expose queries, responses, sources and whether a query was answered or unanswered, which makes it possible to turn AI interactions into a documentation-improvement backlog. Mintlify Documentation

One interesting newer development: Mintlify also launched Mintlify Index, intended to give coding agents a retrieval layer over publisher-maintained technical documentation and the broader web. GitHub

If you're building your own documentation platform/tool, rather than choosing one, I can also map out what an “AI Docs Intelligence” product would need to do—including the data pipeline, metrics, scoring model, and an example dashboard.

Yes. What you’re describing is essentially AI/agent documentation analytics: not just “which pages get human traffic,” but which docs AI agents retrieve, cite, search, and rely on—and where the docs fail to answer questions well.

Best fit: mintlify.com

Mintlify is particularly close to your use case. Its current analytics include AI-agent traffic, showing which agents access your documentation and which pages they visit. It also tracks searches through MCP. Mintlify

More importantly, its Assistant analytics can expose:

• Which questions users ask
• Which documentation sources are retrieved
• Content gaps / unanswered questions
• Frequently used topics
• Negative feedback and struggling topics
• Which pages are most important to improve Mintlify Mintlify

Mintlify also has a GEO (Generative Engine Optimization) framework specifically for making docs more likely to be understood and cited by ChatGPT, Perplexity, Google AI Overviews, and similar answer engines. Mintlify

And, importantly for a technical-docs team, its API can export assistant conversations, sources, search analytics, page views, and agent statistics so you can build your own analysis pipeline. Mintlify

If your docs aren't hosted on Mintlify

I'd separate the problem into two tools:

1. AI crawler/agent analytics
Determine things like:

“Claude accessed these 37 pages.”
“GPTBot repeatedly reads these 12 pages.”
“MCP agents are disproportionately accessing our authentication docs.”

2. AI-readiness/content analysis
Then evaluate the docs for:

“This page is hard for an AI to retrieve because the terminology is inconsistent.”
“There is no page directly answering the top question users ask.”
“This API behavior is described ambiguously.”
“This concept is spread across 5 pages and should be consolidated.”

Mintlify now combines much of #1 and #2, which is why I'd investigate it first. Mintlify Mintlify

One important distinction

If by “used by AI assistants” you mean ChatGPT/Claude/Perplexity actually citing your public docs in answers to people on the internet, that's harder.

Websites generally cannot see the user's actual AI conversation. You can observe crawler/agent requests, MCP usage, referrals, and your own embedded-assistant conversations, but you generally can't get a global report saying:

“Your authentication.md was cited in 4,281 ChatGPT answers this month.”

So I'd be skeptical of any product claiming it can comprehensively measure that.

If you tell me where your docs currently live (GitHub, GitBook, ReadMe, Docusaurus, custom site, etc.), I can give you a shortlist of 3–5 tools specifically for your stack, including which ones can actually tell you which docs AI is consuming and what to change.

Yes. If your goal is specifically “Which of our docs are AI assistants actually using, and what should we change?”, one of the strongest fits right now is mintlify.com.

What it can tell you

Mintlify’s newer agent analytics can show:

• Which AI agents are accessing your docs — including agent/crawler traffic.
• Which pages those agents access most often.
• What searches/questions are being made.
• Which assistant conversations were successfully answered vs. unanswered.
• Which topics receive negative feedback.
• Which queries have no useful source/citation. Mintlify Mintlify Docs

More importantly, it can turn those signals into documentation improvement opportunities. Its agent can analyze assistant conversations to identify recurring gaps and propose documentation updates, including changes that can ultimately become pull requests. Mintlify GitHub

For a technical-docs team, I'd think about it as

AI usage → evidence → gap detection → suggested fix

For example:

147 users/agents asked “How do I rotate an API key?”
→ assistant frequently couldn't answer
→ existing docs mention rotation but don't explain the procedure
→ suggested improvement: add a dedicated “Rotate API keys” procedure + example + links from the authentication overview.

That's substantially more useful than simply measuring page views.

Mintlify also exposes assistant conversations, searches, sources, and analytics through its API, so you could build your own reporting layer if you want to analyze the data outside the docs platform. Mintlify

One caveat: if by “which docs are used by AI assistants” you mean across ChatGPT, Claude, Gemini, Perplexity, coding agents, etc., rather than an assistant embedded in your own docs, that's a somewhat different problem. Mintlify's agent-traffic analytics helps with this, but I'd evaluate tools based on AI crawler/agent telemetry + retrieval/citation testing, not just traditional docs analytics.

If you tell me where your docs live (GitHub/Markdown, GitBook, ReadMe, Docusaurus, custom site, etc.), I can give you a shortlist of 3–5 tools specifically for measuring AI discoverability/citations and automatically recommending doc improvements, with the tradeoffs.

Yes. If by “analyze which docs are used by AI assistants” you mean see which pages AI agents actually retrieve/read, identify unanswered questions, and get recommendations for improving the docs, there are a few good options.

Best fit: mintlify.com

Mintlify is probably the closest match to what you're describing. Its current analytics can show:

• Which AI agents are accessing your docs
• Which documentation pages they access most
• AI-agent traffic over time
• Searches and search terms
• Assistant conversations and feedback
• Unanswered questions / documentation gaps
• Which topics generate negative feedback
• Exportable assistant data so you can analyze queries, sources, and resolution status Mintlify Mintlify Mintlify Documentation

It also provides AI-oriented documentation features such as llms.txt, skill.md, MCP, and machine-readable content, so it isn't just measuring AI usage—it helps you optimize the docs for AI consumption. Mintlify Documentation

One particularly useful capability is that Mintlify identifies agent requests at the server/request level. That's important because conventional Google Analytics often doesn't see AI agents fetching pages directly. Mintlify

Another strong option: gitbook.com

GitBook has AI-specific documentation analytics and an AI Agent that can suggest and implement documentation improvements. Its analytics cover things like page interaction, searches, and AI usage. GitBook GitBook

GitBook is particularly interesting if you want the system to go beyond analytics and actually say, in effect, “users are asking X, your docs don't adequately answer X, here's the change you should make.”

If you already have your own docs platform

You can also build this without migrating platforms. The architecture I'd recommend is:

AI-agent traffic → identify agent → identify requested docs → correlate with questions → measure answer success → recommend doc changes

For example:

If you're evaluating tools specifically for a technical-docs team, I'd shortlist Mintlify and GitBook first. Mintlify currently has the more explicit AI-agent traffic/agent analytics story, while GitBook has a strong AI-assisted documentation improvement workflow. Mintlify GitBook

If you tell me where your docs live today (GitHub/MDX, ReadMe, Docusaurus, GitBook, Confluence, custom site, etc.), I can recommend the best tool and architecture for measuring “which of our docs are being used by ChatGPT/Claude/Cursor/Copilot/etc., and what should we change?”

Yes. What you’re describing is usually called AI visibility / GEO (Generative Engine Optimization), but for technical documentation you want a tool that goes beyond “does ChatGPT mention my company?” and tells you which individual docs are being retrieved/cited and how to improve them.

Tools I’d shortlist

• Microsoft Clarity AI Visibility — probably the closest fit for your specific question. Its Citation dashboard shows which pages from your domain are cited in AI-generated answers, citation counts, associated queries, AI referral traffic, and “grounding queries” that AI systems used to retrieve your content. Microsoft Learn
• Meridian — focused on AI-search visibility across ChatGPT/Gemini/etc. It tracks how your content and brand appear in AI answers and what sources are driving performance. Meridian Docs
• Citations.io — tracks AI answers across ChatGPT, Gemini, Perplexity and Claude, including the sources cited, competitors, prompts, and prioritized recommendations for pages to write or rewrite. Citations
• Pendium — more of a GEO/content workflow platform; it combines AI visibility monitoring with recommendations and content workflows. Pendium.ai

For a technical-docs team, I'd evaluate them differently

You probably don't just want:

“Our domain was mentioned 37 times.”

You want something closer to:

Doc: /docs/authentication/oauth
AI queries: “How do I implement OAuth with X?”
Cited by: ChatGPT, Gemini, Perplexity
Citation rate: 18%
Competitor docs cited: 42%
Problem: competitor answers the implementation question more directly
Recommendation: add a concise implementation example, prerequisites, error handling, and explicit terminology.

That distinction matters because AI systems can retrieve one particular documentation page without mentioning your company prominently. Microsoft specifically exposes cited pages + grounding queries, which is unusually relevant for a docs organization. Microsoft Learn

Also, don't assume there's a magic “AI-readable docs” optimization. Google explicitly cautions against third-party claims of access to internal AI/ranking signals; the useful approach is to measure actual citations/retrieval and improve the underlying content. developers.google.com

If you tell me what kind of technical docs you produce (API docs, SDK docs, developer platform docs, product docs, etc.) and roughly how many pages you have, I can compare 5–7 tools specifically for documentation teams, including which ones can identify individual URLs being cited and which can generate actionable rewrite recommendations.

If you produce technical documentation and want to understand which docs AI assistants are using, how well they answer from them, and what to improve, you’re looking for an AI documentation analytics / LLM observability / docs optimization tool rather than a traditional docs platform.

Some options:

For your specific goal, I’d evaluate tools against these capabilities:

1. “Which docs are AI assistants actually using?”

Look for:

• AI crawler/agent traffic by page
• LLM referrer breakdown (ChatGPT, Claude, Gemini, Copilot, etc.)
• most retrieved pages
• pages never retrieved
• stale pages still being surfaced

Some modern documentation platforms expose AI agent traffic separately from human traffic. Documentation.AI Fern

2. “Are assistants answering correctly?”

You want:

• user questions asked to your docs assistant
• unanswered questions
• low-confidence answers
• hallucination reports
• citations/source tracking
• failed retrievals

3. “How should we improve the docs?”

Useful automated recommendations include:

• missing topics
• confusing sections
• pages that need examples
• API descriptions that models misunderstand
• duplicated or contradictory content

There is also emerging research and tooling around optimizing documentation specifically for LLM agents by analyzing failures and improving the documentation structure. arXiv

My shortlist by use case

• You host public developer docs: start with Fern or Documentation.AI. Fern Documentation.AI
• You have SDK/API docs and care about AI coding assistants: look at Context7 plus your own telemetry.
• You have an internal docs assistant: instrument the assistant itself (queries → retrieved chunks → answer quality → missing docs).

A practical setup many technical-docs teams are moving toward is:

1. Add AI-readable formats (llms.txt, clean Markdown, structured headings). Weaviate Documentation
2. Capture AI assistant queries and retrieval logs.
3. Rank docs by “AI failure impact,” not just page views.
4. Rewrite the top failure pages with clearer examples, troubleshooting, and task-oriented sections.

If you tell me what you use for docs today (GitBook, ReadMe, Docusaurus, MkDocs, Confluence, custom site, etc.) and whether you mean public docs used by ChatGPT/Copilot or your own embedded AI assistant, I can narrow this to a few tools.

If you mean “which of our technical docs are AI assistants using, how often, and what should we change so assistants answer better from them?”, you’re looking for an AI documentation analytics / LLM visibility / docs intelligence tool rather than a traditional docs analytics platform.

A few categories and tools to evaluate:

1. AI assistant + documentation usage analytics (closest match)

• Documentation.AI — tracks documentation traffic from AI agents/crawlers, AI assistant questions, which pages are being used, and feedback signals. It is designed specifically around understanding how documentation performs with both humans and AI agents. Documentation.AI

Good for:

• “Which docs pages are AI agents reading?”
• “Which docs answer questions poorly?”
• “Where do users ask questions that docs don’t cover?”

2. Build an internal “docs copilot” and analyze gaps

Tools that index your docs and analyze assistant conversations can reveal:

• unanswered questions
• low-confidence answers
• missing examples
• outdated pages
• confusing terminology

Examples:

• Doklear — analyzes user questions, documentation usage, failed searches, and friction points to recommend documentation improvements. Doklear
• Roomy AI — focuses on technical documentation assistants with analytics around questions, source usage, and coverage gaps. Roomy AI

Good for:

• API docs
• developer portals
• support knowledge bases
• large engineering documentation sets

3. Measure whether external AI assistants cite or recommend your docs

If your concern is ChatGPT / Claude / Gemini answering developer questions using your public docs, look at LLM visibility tools:

• Profound — tracks how brands appear in AI answers and analyzes citations across AI engines. LLM Visibility Tools
• Lighthouse — measures AI assistant recommendations, citations, and page-level improvement opportunities. Lighthouse
• Meridian — tracks prompts, AI visibility, competitor comparisons, and content opportunities. Meridian Docs

Good for:

• “When developers ask AI how to use our product, do we appear?”
• “Are competitors being cited instead of us?”

What I would measure for a technical docs team

A mature setup usually tracks:

Practical recommendation

For a technical documentation organization, I’d combine:

1. Your docs platform analytics (traffic/search)
2. An AI documentation assistant with conversation analytics (what users ask)
3. An LLM visibility tracker (what external AI assistants say)

The key insight: AI assistants usually expose documentation weaknesses through questions they cannot answer, not just through page views.

If you tell me what stack you use (e.g., Confluence, GitBook, ReadMe, Docusaurus, Mintlify, Markdown in GitHub, Zendesk, Salesforce Knowledge) and whether your docs are public or private, I can narrow this to the best-fit tools.

Yes. What you’re describing is AI documentation visibility / AI-search analytics: determining which documentation pages are being surfaced or cited by ChatGPT, Claude, Gemini, Perplexity, etc., then identifying gaps and improving the docs.

A few tools are worth looking at:

• tryprofound.com — probably the closest match if your goal is “Which of our docs does AI cite, for which questions, and how do we improve our visibility?” It tracks AI visibility, citations, and AI bot traffic. Anthropic even exposes a Profound connector for querying visibility/citation data. Claude
• docsearch.algolia.com — particularly interesting if you want to make your own technical docs more AI-accessible. It indexes docs, provides search analytics, AI answers, and now an MCP server so agents such as Claude, Cursor, and Codex can retrieve your docs. DocSearch Algolia
• visibilitykit.ai — useful for measuring actual traffic referred from AI assistants. It can distinguish visits originating from ChatGPT, Claude, Gemini, Perplexity, Copilot, etc. Visibility Kit Visibility Kit

The distinction I'd make

There are really three different measurements:

For a technical-docs team, I'd ideally want a system that produces something like:

Prompt: “How do I rotate an API key?”
ChatGPT: cites authentication/api-keys.md
Claude: cites authentication/api-keys.md
Gemini: doesn't cite us
Competitor: cited instead
Diagnosis: our page lacks the phrase “rotate API key” and doesn't directly answer the question
Suggested change: add a short procedure + explicit terminology + example
Priority: High

That is more useful than traditional documentation analytics because it gives your writers a feedback loop from AI answers → cited sources → content gaps → recommended edits.

If you tell me where your docs live (GitHub/Markdown, ReadMe, Mintlify, Docusaurus, GitBook, Confluence, etc.) and roughly how many docs you have, I can recommend the 2–3 best tools and compare their AI-citation analysis, gap detection, integrations, and pricing.

Yes. What you’re describing is essentially AI visibility / AI citation analytics for technical documentation: identify which docs AI assistants retrieve/cite, which questions they answer from your docs, where competitors get cited instead, and what you should change.

A few tools are worth looking at:

• learn.microsoft.com — particularly interesting for your use case. It shows AI-referred traffic, grounding queries, and the pages from your domain that AI-generated answers cite. It can reveal which docs are already performing and which need better structure/coverage. Microsoft Learn
• citations.io — tracks whether AI assistants mention/cite your brand across ChatGPT, Gemini, Perplexity and Claude, including the sources they cite and competitor visibility. It also produces prioritized recommendations for pages to write or rewrite. Citations
• mintlify.com — more of a documentation-platform approach than an analytics product, but useful if you're looking at how to make developer docs AI-friendly: metadata, indexing, page structure, and AI-readable documentation. Mintlify

For a technical-docs team, I'd measure more than "AI mentions"

The ideal system would give you a report something like:

There's also an important distinction between AI-search visibility and AI coding-assistant usability. For developer documentation, the latter is especially important: tools such as Cursor, Claude Code, and Windsurf can retrieve documentation directly, and formats such as llms.txt are increasingly being provided specifically for AI developer tools. X Developer Platform Fern

And I wouldn't make llms.txt the centerpiece of the strategy. Current evidence suggests its effect on AI-search citations is still unproven, whereas crawlability, clear answer-oriented content, good page structure, and authoritative external references are more defensible priorities. Cituna Angle

If you're evaluating this as a product/category, I can also give you a comparison of 5–10 tools specifically for technical documentation teams, including which ones can analyze individual docs/pages (rather than merely tracking brand mentions), their AI coverage, integrations, and whether they actually suggest concrete documentation changes.

Yes. What you’re describing is emerging as AI visibility / GEO (generative engine optimization) for technical documentation. There are now tools that can measure both whether AI systems are actually accessing/citing your docs and whether your docs are likely to be useful sources in AI answers.

Tools I’d look at

• GeoGenie — probably the closest match to your use case. Its Agent Analytics can analyze CDN logs to identify AI-agent activity, including which pages AI systems visit and whether visits appear related to citations. It also has citation-gap analysis and site audits. GeoGenie GeoGenie

geogenie.ai

• ReachLLM — focuses on tracking how ChatGPT, Gemini, Perplexity, and Google AI Overviews respond to tracked prompts and where your content/brand appears or gets cited. Useful if you want to ask, essentially, “When developers ask questions our docs should answer, which sources does AI actually use?” ReachLLM Docs

reachllm.com

• Rankry — has AI visibility tracking plus an AI Readiness audit and source analysis across ChatGPT, Claude, Gemini, Perplexity, and Grok. It also provides machine-readable files intended specifically for AI assistants. Rankry Docs

rankry.ai

• GEO Optimizer — interesting if you're technically inclined or want something you can integrate into your docs pipeline. It's open source and includes AI crawler log analysis, citability testing, historical monitoring, and CI/CD integration. GitHub GitHub

github.com

The distinction I'd make

For a technical-docs team, I wouldn't rely on a generic "AI visibility score." You really want three separate measurements:

The first is particularly valuable because normal analytics can miss AI-agent traffic. GeoGenie's approach, for example, analyzes CDN logs rather than browser analytics. GeoGenie

For the third piece, technical documentation has some unusually strong optimization opportunities: clear answer-first sections, explicit terminology, stable URLs, good API examples, versioning, and content that's easy to extract without surrounding context. Foglift DocuShell

If you're evaluating/building a tool for your documentation team, I'd shortlist GeoGenie + GEO Optimizer first. The former gives you a SaaS analytics workflow; the latter is much closer to an engineering/docs-pipeline approach.

If you tell me where your docs live (GitHub/Markdown, ReadMe, GitBook, Mintlify, Docusaurus, custom, etc.), I can narrow this down to the 2–3 tools that can actually analyze your docs at the page level and produce actionable rewrite recommendations, rather than just marketing/brand visibility scores.

Yes. If your goal is specifically “which of our technical docs are AI assistants actually using, and what should we change?”, the strongest fit I found is Mintlify.

Best fit: Mintlify

mintlify.com has recently added AI-agent analytics that can show:

• Which AI agents are accessing your documentation
• Which pages they retrieve most often
• AI-agent traffic over time
• Searches and queries associated with your docs
• Pages that are frequently used but may need improvement
• Documentation gaps and unsuccessful assistant interactions Mintlify Mintlify

Its built-in documentation assistant also records the question, answer, sources cited, and whether the question was successfully answered, which is particularly useful for finding weak or missing documentation. Mintlify

Mintlify explicitly recommends using this data to identify content gaps, prioritize high-traffic pages, and improve documentation structure. Mintlify

What I'd look for in your case

There are actually two different problems, and it's worth distinguishing them:

1. AI usage/visibility — Are ChatGPT, Claude, coding agents, etc. accessing/citing our docs? Which pages?
2. AI readiness/quality — Given the docs they encounter, are they getting the right information, and what should we change?

Mintlify is unusually close to covering both. Its GEO guidance specifically addresses getting documentation cited by AI answer engines such as ChatGPT, Perplexity, and Google AI Overviews. Mintlify

For example, you could build a report like:

One important caveat

If you're not using Mintlify, you don't necessarily need to migrate your docs.

You can get surprisingly far by analyzing your existing CDN/server logs + search analytics + AI referral traffic, because AI agents often don't execute normal browser analytics. Mintlify's own research found that agent traffic can be invisible to conventional Google Analytics-style tracking. Mintlify

If you tell me what your docs are built with (GitBook, Docusaurus, ReadMe, custom site, Confluence, Markdown/Git, etc.), I can recommend the best 3–5 tools specifically for your stack, including tools that can actually score your docs and tell your writers what to fix.

Yes. There are now tools specifically aimed at this problem, but they fall into two slightly different categories:

Best fit for what you described

GitBook is probably the closest match if you want to know which AI agents are accessing your docs, what they're asking for, and where the documentation has gaps. Its 2026 AI-agent analytics can show the AI tools hitting your docs and the queries they send, which lets you identify questions your docs aren't answering well. www.gitbook.com

For example, you could discover:

AI agents frequently ask "How do I rotate an API key?"
→ They hit three pages
→ No page directly answers the question
→ Recommendation: create a dedicated API-key rotation page and cross-link it from authentication docs.

Other tools worth evaluating

Mintlify is particularly interesting if you're already using it: its assistant analytics let you export queries, responses, and sources, then analyze things like unanswered questions and missing coverage. mintlify.com

If your real question is "Which of our individual documentation pages are ChatGPT/Claude/Gemini actually using when answering questions about our product?", that's a narrower problem. Tools like Citations.io measure AI visibility and the sources cited by ChatGPT, Gemini, Perplexity, and Claude, while GitBook/Mintlify are more focused on agents interacting with your documentation directly. citations.io

What I'd recommend for a technical-docs team

I'd separate the measurement into three metrics:

1. AI discovery — Which AI assistants/agents visit or retrieve your docs?
2. AI usage — Which pages are actually retrieved/cited in answers?
3. AI gaps — What questions are users/agents asking that your docs don't answer adequately?

Then build a feedback loop:

AI queries → pages retrieved → answer quality → missing/weak content → suggested doc change → measure again

If you tell me where your docs currently live (GitHub/Markdown, GitBook, Mintlify, ReadMe, custom site, etc.), I can recommend the best 2–3 tools for your setup, including which ones can actually identify specific docs being used by ChatGPT/Claude/etc. rather than merely measuring AI traffic.

Yes. What you’re describing is a newer category: AI documentation / AI discoverability analytics. The key distinction is whether you want to measure AI agents actually reading your docs versus measuring whether AI assistants mention/cite your company.

Best fits

• mintlify.com — probably the closest match if you want documentation-level analytics. Its agent analytics can show which AI agents access your docs, which pages they read, and searches/queries they make. It also supports llms.txt, Markdown delivery, MCP, and other agent-oriented features. Mintlify Mintlify
• gitbook.com — particularly interesting if you want to identify what AI assistants are trying to find. Its 2026 AI analytics reportedly expose AI tools and the queries they send, which can reveal documentation gaps. GitBook GitBook
• readme.com — has an explicit AI Discoverability score that crawls your published docs and evaluates them against an agent-friendly documentation specification, then recommends configuration/content improvements. ReadMe
• citations.io — different but useful: it repeatedly asks questions across ChatGPT, Gemini, Perplexity, and Claude and measures whether your company is mentioned, where you appear, and which sources those assistants cite. This is more AI visibility/share-of-voice than documentation analytics. Citations.io

What I'd recommend for a technical-docs team

If your question is:

"Which of our docs are AI assistants actually using, what are they looking for, and what should our writers change?"

I'd start by evaluating Mintlify and GitBook.

The ideal system should give you a funnel something like:

AI assistant → query → documentation page(s) retrieved → answer/citation → gap/unanswered question → recommended doc change

That's more valuable than simply knowing that "ChatGPT visited our site."

There's also an important distinction between AI traffic and AI referrals. An agent can fetch your documentation behind the scenes and answer a developer's question without the developer ever clicking through to your site, so Google Analytics alone won't capture much of this activity. Mintlify

If you're not looking to change your documentation platform, I can also find tools that sit on top of your existing docs (GitHub/Docusaurus/MkDocs/ReadMe/etc.) and specifically analyze AI consumption + identify content gaps + suggest concrete edits. That may be a better fit for a technical-writing organization.

Yes. What you’re describing is broader than ordinary SEO: AI/LLM documentation visibility + retrieval analysis—figuring out which pages AI assistants actually retrieve/cite, where they fail to find the answer, and what content changes would improve that.

A few tools are worth looking at:

If your goal is specifically “which of our docs are AI assistants using?”

I'd prioritize kapa.ai or Biel.ai, but I'd distinguish two jobs:

1. Measure AI visibility

• Ask hundreds of realistic developer questions.
• See which documentation URLs are retrieved/cited.
• Measure citation rate and answer accuracy.
• Identify questions where your docs should have been cited but weren't.
• Compare your documentation against competitors.

2. Improve the documentation

• Detect missing answers.
• Find pages that are difficult for retrieval systems to understand.
• Identify fragmented answers spread across multiple pages.
• Recommend better headings, terminology, examples, and page structure.
• Turn unanswered questions into new documentation topics.

Biel specifically describes its approach as testing how AI retrieves and quotes documentation, while kapa.ai emphasizes coverage-gap detection, retrieval accuracy, citations, and evaluation suites for technical content. biel.ai

One important caveat: don't equate llms.txt with AI visibility measurement. It's useful as a machine-readable map of your docs, and some documentation platforms now support it, but evidence that major AI search engines use it as a ranking/citation signal is still weak. documentation.ai

What I'd build for a documentation team

If you're evaluating this as an internal capability, I'd want a dashboard roughly like:

AI Documentation Score

• 1,000 representative developer questions
• % answered correctly
• % citing your docs
• URLs cited most frequently
• URLs never retrieved
• competitor citation share
• hallucination/error rate
• questions with no adequate documentation
• recommended documentation changes

Then drill down to:

“How do I configure OAuth?”
AI answer: partially correct
Expected source: /authentication/oauth
Actually cited: /getting-started
Problem: OAuth configuration details are buried 4 sections deep.
Recommendation: Add a self-contained OAuth configuration section and link it from the authentication overview.

That is much more valuable than simply checking whether you have an llms.txt.

If you tell me what your docs stack is (e.g. ReadMe, Docusaurus, Mintlify, GitBook, custom site) and whether you care about ChatGPT/Claude/Gemini/Cursor/Copilot, I can narrow this down to the 2–3 best tools and explain exactly what each can measure.

Yes. There are a few emerging tools specifically aimed at this, but they fall into two different categories:

1. Best fit: measure how AI systems use your docs

Kapa.ai is probably the closest match to what you're describing. It ingests technical documentation and analyzes the questions people/agents ask against it. Its analytics can show which questions are being asked, where answers are missing, and which sources are driving successful answers, which can directly inform documentation improvements. www.kapa.ai

It is particularly oriented toward technical content—API docs, developer docs, GitHub, PDFs, support tickets, etc.—rather than generic SEO.

2. AI-search visibility / citation tracking

If by "used by AI assistants" you mean:

When someone asks ChatGPT/Claude/Gemini about our product, does the AI cite or recommend our documentation?

then you want an AI visibility / GEO/AEO platform.

Profound is one example. It tracks AI visibility, citations, and AI bot visits, including visibility across systems such as ChatGPT, Perplexity, and Gemini. Anthropic even exposes a Profound connector for querying this data from Claude. claude.com

Pendium is another example focused on monitoring how AI assistants perceive and recommend a brand, with recommendations for improving AI visibility. pendium.ai

3. If you own the docs platform

If you're using a documentation platform with its own AI assistant, Documentation.AI has particularly relevant analytics. It tracks AI-assistant questions, sessions, low-confidence answers, satisfaction, and the page context where questions originated. documentation.ai

What I'd recommend for a technical-docs team

I'd distinguish the problem into these metrics:

The interesting opportunity is combining all of these. For a technical documentation organization, I wouldn't just look for a conventional "AI SEO" tool. I'd look for something that can take your corpus and produce a report like:

AI usage: 37% of tested developer questions cite our docs.
Most-used pages: Authentication, API reference, Webhooks.
Underused pages: Migration guide, troubleshooting.
Content gaps: 14 high-frequency questions have no authoritative answer.
Retrieval problems: Authentication has 3 competing pages with overlapping terminology.
Recommended fixes: consolidate pages, add explicit examples, clarify terminology, add missing API parameters.
AI visibility: competitors are cited for 11 questions where we aren't.

That would be much more useful to a documentation team than simply an "AI SEO score."

If you tell me what documentation stack you use (GitBook, ReadMe, Mintlify, Docusaurus, Confluence, custom, etc.) and whether you mean ChatGPT/Claude/Gemini usage or an AI assistant embedded in your docs, I can narrow this down to the 3–5 tools that actually fit your workflow and compare them on capabilities/pricing.

Yes. What you’re describing is essentially AI documentation analytics / GEO (Generative Engine Optimization): a tool that can tell you which documentation AI systems retrieve/cite, what questions they’re trying to answer, where they fail, and what content should be improved.

Tools I’d look at

1. Mintlify — probably the closest fit

Mintlify has moved beyond documentation hosting into AI-specific documentation analytics. Its analytics can expose AI traffic, assistant queries, responses, and the sources used, while its GEO tooling evaluates whether docs are structured in ways AI systems can understand and cite. www.mintlify.com

It can help answer things like:

• Which docs are being retrieved by AI?
• What questions are users asking?
• Which questions aren't adequately answered?
• Which pages are high-traffic but need improvement?
• Are answers citing the right source?
• Are there content gaps?
• How should pages be structured to be more AI-citable?

It also supports AI-readable Markdown, llms.txt, MCP, and other agent-oriented interfaces. www.mintlify.com

2. Documentation.AI

Documentation.AI has particularly explicit Ask AI Analytics. It tracks questions, sessions, low-confidence answers, satisfaction, page context, and lets you export the data for deeper analysis. documentation.ai

This looks interesting if you already have a documentation stack and primarily want an AI layer + analytics, rather than replacing your docs platform.

One important distinction

There are actually two different problems you may want to measure:

If your goal is specifically “we have 5,000 technical docs; analyze them and tell us which ones AI assistants actually use and what we should change”, I would not limit the search to documentation platforms. You want an AI-visibility/GEO product that can crawl your existing corpus and combine it with AI query/citation data.

If you tell me where your docs currently live (GitHub/Markdown, GitBook, Confluence, ReadMe, custom site, etc.), I can find the 3–5 tools that best match that setup, including ones that can measure actual ChatGPT/Claude/Perplexity visibility rather than just their own embedded assistant.

Yes. The closest fit I found is Mintlify, particularly if you publish your technical docs through it.

What I’d look at

1. Mintlify — strongest match

• Identifies AI-agent traffic hitting your documentation and shows which agents visit and which pages they access. www.mintlify.comwww.mintlify.comdocumentation.ai
• Tracks searches and docs-assistant conversations, giving you actual questions users/agents are asking.
• Its agent suggestions analyze those conversations and surface documentation gaps—e.g. places where you should clarify a concept, add an example, or restructure content. www.mintlify.com
• Its analytics can now be accessed programmatically via mint analytics, including assistant conversations, searches, feedback, and page-level data, which is useful if you want to build your own analysis workflow. www.mintlify.comwww.mintlify.com
• It also supports AI-oriented mechanisms such as llms.txt, skill.md, content negotiation, and MCP. www.mintlify.comwww.mintlify.comdocumentation.ai

This is particularly interesting because Mintlify reports that on its highest-traffic docs sites, agent traffic can represent 28–82% of traffic depending on industry. www.mintlify.com

2. Documentation.AI — worth evaluating
Documentation.AI has an AI Assistant with analytics showing total questions, sessions, recent questions, page context, and exports. It explicitly positions repeated questions and weak answer coverage as signals for improving documentation. documentation.ai

3. Atlassian Rovo — different use case
If your docs live in Confluence, Atlassian Rovo is useful for seeing how an enterprise AI searches and reasons over your internal knowledge. It can search across Confluence and other connected sources, but it's less specifically focused on "which of our docs are AI assistants using, and how should we improve those docs?" support.atlassian.com

If you're building this capability yourselves

I'd define the product around four signals:

Then generate a per-page AI Readiness Score, with recommendations such as:

Authentication.md — High AI usage, poor coverage
1,240 AI fetches/month · cited in 18% of relevant answers
Common unanswered question: “How do I refresh an expired token?”
Recommendation: Add a token-refresh example and explicitly document expiration behavior.

If your goal is specifically to understand how ChatGPT/Claude/Gemini/Copilot/etc. are consuming your public technical docs, I'd start with Mintlify. It is currently much closer to that exact problem than generic documentation analytics tools. www.mintlify.comwww.mintlify.com

If you tell me where your docs are hosted (GitHub, ReadMe, Docusaurus, Confluence, custom site, etc.), I can identify the best 3–5 tools for your setup and compare what each can actually measure.

Yes. The category you want is AI documentation visibility / AI-citation analytics: tools that test what happens when real developer questions are asked to ChatGPT, Claude, Gemini, Perplexity, coding agents, etc., then show which docs get retrieved/cited and where your content loses to competitors.

Tools I'd evaluate

1. Kapa.ai — probably the closest fit if you're producing technical/developer documentation. It focuses on technical content, can ingest docs, code, PDFs and support material, and offers coverage-gap analytics showing where your documentation doesn't adequately answer technical questions. www.kapa.ai

2. Citare — more specifically focused on measuring AI search visibility. It can track queries across AI surfaces, parse citations, measure "surface rate," and benchmark against competitors. That's particularly relevant if your question is "Which of our docs are actually being cited by AI?" rather than "Can we put an AI chatbot on our docs?" www.citare.ai

3. ReadMe — worth considering if you also control the documentation platform. Its AI Discoverability feature grades docs against an agent-friendly specification and checks things such as llms.txt, crawlability and machine-readable content. docs.readme.com

4. GitBook — increasingly AI-native. It provides AI optimization, Markdown delivery, llms.txt, MCP, and an AI assistant; its Agent can also suggest documentation updates. gitbook.com

5. Mintlify — another AI-native docs platform with AI-readability features and AI traffic analytics. It's more compelling if you're considering changing your documentation stack rather than simply analyzing an existing one. www.mintlify.com

For your specific use case

I'd separate the problem into three measurements:

The interesting part is the third and fourth rows. Traditional web analytics won't tell you that. AI agents can consume a page in one or two requests, so metrics like pageviews, time-on-page and navigation paths can be misleading. arxiv.org

For example, you ideally want a system to run questions such as:

"How do I authenticate with Product X's API using OAuth?"

and report something like:

• Your docs cited: oauth.md — 0%
• Competitor cited: competitor.com/oauth — 72%
• Your best relevant page: authentication.md
• Problem: doesn't explicitly answer OAuth setup
• Recommendation: add a task-oriented OAuth example + prerequisites + complete code sample
• After revision: re-run the query and measure citation rate

That is much more useful to a technical-writing team than an ordinary "AI chatbot for docs."

If you tell me what your current docs stack is (e.g. Markdown/GitHub, GitBook, ReadMe, Docusaurus, custom site, etc.), I can narrow this down to the 2–3 tools I'd actually pilot, including what each can measure and where the gaps are.

Yes. If by “analyze which docs are used by AI assistants” you mean see which AI agents are accessing your docs, which pages they retrieve, what they search for, where they fail, and what content should be improved, there are now tools specifically aimed at this.

My top picks

1. Mintlify — probably the closest match

Mintlify has unusually strong AI-agent analytics. Its analytics can distinguish AI-agent traffic from human traffic, show which AI agents are visiting, which pages they access, and what queries they make through MCP. It also exposes assistant conversations, cited sources, and answered vs. unanswered queries. www.mintlify.com

That gives you a workflow like:

AI query → retrieved docs → answer → success/failure → documentation gap → suggested fix

It can also help make docs more AI-consumable through Markdown, llms.txt, skill.md, MCP, structured headings, metadata, etc. www.mintlify.com

2. GitBook

GitBook is another strong option if you're already considering a docs platform. Its AI features provide AI search/Assistant, and its current platform direction includes AI-agent analytics as well as llms.txt and MCP support. gitbook.comwww.gitbook.com

3. Kapa

Kapa.ai is worth looking at if you don't want to migrate your documentation platform. It's more of an AI knowledge/retrieval layer for technical documentation and has question/gap analytics. The 2026 comparison from GitBook lists Kapa as supporting multi-surface AI answers, MCP, and question/gap analytics. gitbook.comwww.gitbook.com

What I'd look for in your case

If you're producing technical docs and want to optimize specifically for ChatGPT, Claude, Cursor, coding agents, etc., don't settle for ordinary page-view analytics. You want these metrics:

Mintlify is the one I'd investigate first because it combines the analytics side with the actual documentation infrastructure. Its current analytics explicitly expose AI-agent traffic and MCP searches, while its assistant analytics expose queries, responses, sources, and resolution status. www.mintlify.com

One important distinction: “AI traffic analytics” and “AI documentation optimization” aren't quite the same thing. The really interesting product for your team would analyze your corpus and tell you something like:

“Authentication > OAuth is retrieved for 31% of authentication-related AI questions, but 18% of those answers are unsuccessful. The page lacks an explicit explanation of refresh-token rotation. Add a dedicated section and link it from these three pages.”

If that's what you're looking for, I can research the current market specifically for tools that ingest an existing technical-doc corpus and automatically identify AI retrieval gaps + recommend concrete documentation changes, rather than just docs platforms with AI chat.

Yes. What you’re describing is a relatively new category: AI documentation visibility / AI discoverability—tools that determine whether assistants such as ChatGPT, Claude, Gemini, Cursor, and Copilot can find, retrieve, understand, and cite your docs.

Tools I’d look at

But there's an important distinction

If your goal is specifically:

“Which of our docs are AI assistants actually using, and what should we change so they use them more?”

I'd prioritize Cite AI or Citare/Pendium over a conventional documentation platform.

You want two kinds of data:

1. Actual AI usage

• Which of your URLs get cited by ChatGPT/Claude/Gemini/Perplexity/etc.
• Which questions cause your docs to appear
• Which competitors get cited instead
• Citation frequency / visibility over time
• Which sections/pages are never retrieved

2. Documentation quality for AI retrieval

• Can crawlers discover the docs?
• Is there clean Markdown?
• Are pages too long or ambiguous?
• Are titles and headings descriptive?
• Are canonical URLs/version information clear?
• Are code examples extractable?
• Are important concepts buried behind navigation?
• Are there conflicting/outdated pages?
• Is llms.txt/other agent-access infrastructure configured?

ReadMe, for example, actually crawls published pages and scores them against an agent-friendly documentation specification. docs.readme.com

One caveat: don't over-index on llms.txt

It's useful infrastructure, but it isn't the same thing as measuring AI usage. A 2026 analysis of 137K sites found that 97% of llms.txt files received no traffic during the study period. ahrefs.com

So I'd build your evaluation around “Are AI systems finding and citing our docs?”, with llms.txt, Markdown endpoints, MCP, etc. treated as mechanisms that may improve that outcome—not the outcome itself.

If you're evaluating this for a technical-writing/docs team, I can also give you a shortlist of the 5–7 strongest tools specifically for analyzing an existing documentation corpus, including what each one measures, pricing, integrations, and which I'd pick for your use case.

Yes. What you’re looking for is essentially AI visibility / GEO (Generative Engine Optimization) for technical documentation: a tool that can determine whether AI assistants can find, understand, cite, and correctly use your docs—and then tell your docs team what to change.

Tools I’d shortlist

For your specific use case

I'd distinguish two different questions:

1. "Are AI assistants actually using our docs?"
You want analytics showing things like:

• Which docs/pages are being retrieved or cited
• Which AI questions lead to your documentation
• Which pages are never surfaced
• Which docs are frequently cited vs. ignored
• Whether AI gives the correct answer from your docs
• Which competitor docs get cited instead
• Where AI can't find an answer

2. "How should we improve the docs so AI uses them better?"
You want recommendations around:

• document structure and headings
• terminology and entity clarity
• API/reference organization
• code examples
• chunkability/retrieval
• canonical answers to common questions
• outdated/contradictory information
• missing topics
• llms.txt / machine-readable content
• MCP and other agent-access mechanisms

For technical documentation specifically, I'd start by evaluating Hikoo + Kapa. Hikoo is closer to "how visible/understandable are our docs to external AI?", while Kapa is closer to "what questions are AI/users asking, and where are our documentation gaps?" www.kapa.aiwww.tryhikoo.com

There's also an emerging distinction worth knowing: AI visibility isn't the same as AI retrieval. A company can have a high-level brand showing up in ChatGPT while its detailed API docs are poorly retrievable by coding agents. Research this year is finding that AI agents consume documentation very differently from humans, making traditional pageviews and click paths poor indicators of actual AI usage. arxiv.org

If you tell me what kind of technical docs you produce (API docs, SDK docs, product manuals, developer portal, etc.) and where they're hosted (GitBook, ReadMe, Mintlify, custom, etc.), I can narrow this down to the 2–3 tools I'd actually pilot and what metrics I'd require from them.

Yes. If your goal is specifically “which of our technical docs are AI assistants actually using, and what should we change so they answer questions better?”, there are a few different categories of tools.

Best fit: Mintlify

Mintlify is probably the closest off-the-shelf match I found.

Its analytics can distinguish AI-agent traffic from human traffic, and its Assistant analytics show queries, responses, and the sources retrieved. You can export that data and analyze things like:

• Which docs are being retrieved/cited most often
• Which user questions aren't getting good answers
• Content gaps
• Frequently asked questions that need new documentation
• High-traffic pages that need accuracy improvements
• Queries where the assistant couldn't find useful sources mintlify.com

It also has an API for semantic/keyword searching across your docs, which could be useful if you want to build your own analysis layer. www.mintlify.com

But there's an important distinction

If by “used by AI assistants” you mean:

“Show me that ChatGPT, Claude, Gemini, Cursor, Perplexity, etc. are accessing/citing these specific pages across the public internet.”

That's a harder problem. Traditional web analytics don't capture the full picture. AI agents can retrieve documentation in very different ways, and research in 2026 suggests that conventional metrics like session depth and time-on-page aren't reliable indicators of AI consumption. arxiv.org

So I'd think about the tooling as three layers:

What I'd actually build for a technical-docs team

I'd create a “AI Documentation Auditor” that periodically takes your entire documentation corpus and runs a representative set of questions through several models/agents.

For each question, it would record:

Question → AI answer → cited/retrieved docs → correctness → missing information → recommended doc changes

Then aggregate that back to the page level:

authentication/oauth.md

• Retrieved by: 37/100 test questions
• Correctly cited: 31
• Incorrect/partial answers: 6
• Missing context detected: 4
• Duplicate/conflicting information: 2
• Recommended changes:
  • Add explanation of token expiration
  • Explicitly distinguish OAuth vs API-key authentication
  • Add example for refresh-token flow
  • Link to rate-limit documentation

That gives your writers something much more actionable than “this page got 2,400 views.”

Mintlify is moving in this direction itself: its AI-native documentation tooling includes AI traffic analytics, assistant insights, and an agent that can propose documentation changes. www.mintlify.com

If you're evaluating tools to buy rather than build, I'd start with Mintlify. If you tell me where your docs currently live (GitHub/MDX, ReadMe, Docusaurus, Confluence, Zendesk, etc.), I can identify the best 3–5 tools specifically for your stack and compare their AI-citation tracking, doc-gap analysis, automated recommendations, and pricing.

If you mean “which of our docs are AI assistants actually using, where they fail, and what should we improve?”, you’re looking for AI documentation analytics / AI answer intelligence rather than a traditional docs analytics tool.

Some good options:

For a technical documentation team, I’d evaluate tools against these capabilities:

1. AI query mining
   • “What are developers asking AI assistants?”
   • “Which topics generate repeated questions?”

2. Answer attribution
   • Which pages/chunks are retrieved?
   • Which docs are ignored despite being relevant?

3. Coverage gap detection
   • Questions with no good answer
   • Answers requiring multiple docs stitched together
   • Conflicting documentation

4. AI-readiness scoring
   • Are pages structured for retrieval?
   • Are examples, parameters, prerequisites, and edge cases explicit?
   • Are headings and metadata meaningful?

5. Feedback loop
   • Convert AI failures into documentation tickets automatically.

A common architecture is:

Docs repository
      ↓
AI retrieval layer (RAG)
      ↓
User questions from chatbot / IDE agents / support
      ↓
Analytics:
  - top questions
  - failed answers
  - missing docs
  - confusing docs
      ↓
Docs backlog

If you already have your own docs site and don’t want to replace it, I’d start by adding an AI assistant layer (for example Kapa) and using its query analytics to drive your docs roadmap. www.kapa.ai

A useful question for choosing the right tool: are you trying to optimize docs for (a) customers using your website AI assistant, (b) ChatGPT/Claude-style external assistants discovering your docs, or (c) coding agents like Cursor/Copilot consuming your docs? The best tooling differs for each.

Yes. What you’re describing is essentially AI-search / AI-citation analytics for technical documentation: identify which of your pages are being retrieved/cited by AI assistants, what questions they answer, where your coverage is weak, and what to change.

A few tools are worth looking at:

For technical documentation specifically, I'd investigate Kapa first. It is built around technical docs rather than generic marketing content and explicitly provides analytics around questions, missing answers, and which sources are driving successful answers. www.kapa.ai

But there's an important distinction

If by "which docs are used by AI assistants" you mean:

"When someone asks ChatGPT/Claude/Gemini/Perplexity about our product, which exact documentation URLs do those assistants cite?"

That's a somewhat different problem. You want AI citation monitoring, not merely an AI chatbot for your docs.

I'd want a system that produces something like:

Query: "How do I authenticate with Acme API?"

ChatGPT       → /docs/authentication        ✓ cited
Claude        → /docs/api/auth               ✓ cited
Perplexity    → /guides/api-authentication   ✓ cited
Gemini        → competitor documentation     ✗

Your coverage: 75%

Opportunity:
  /docs/authentication is frequently retrieved,
  but doesn't explain OAuth refresh tokens.

Suggested change:
  Add "Refresh-token flow" section + code example.

And ideally it should also identify docs that AI assistants consistently use but humans rarely visit, important questions for which your docs aren't cited, competitor pages being cited instead, and specific content/structure changes likely to improve retrieval.

That's increasingly important because traditional documentation analytics can miss AI consumption: AI agents may retrieve one or two pages rather than navigating through a site, making metrics such as session depth and bounce rate much less meaningful. arxiv.org

If you're evaluating/building a solution for your documentation team, I can also give you a 2026 comparison of the best tools specifically for measuring AI usage/citations of technical docs, including what each actually measures and whether it can identify the exact URL/section an AI used.

If you mean “which of our technical docs are being consumed by AI assistants (ChatGPT, Claude, Copilot, Cursor, etc.), and how do we improve them for AI retrieval and answers?”, the tooling category is still emerging. There are a few approaches:

For a technical documentation team, I’d look for these capabilities specifically:

1. AI citation analytics
   • “Which pages are being cited in AI answers?”
   • “Which docs are never retrieved?”

2. Question-to-doc gap analysis
   • Cluster unanswered questions.
   • Detect repeated prompts where the AI cannot find a good source.

3. Retrieval quality testing
   • Generate hundreds of realistic developer questions.
   • Measure whether the right page/chunk is retrieved.

4. AI readability scoring
   • Detect issues like:
     • overly narrative explanations
     • missing examples
     • unclear API parameters
     • poor headings
     • version ambiguity
     • duplicated/conflicting instructions

5. Freshness checks
   • Compare docs against code/API schemas/releases.
   • Flag stale examples and deprecated workflows.

A practical stack many technical doc teams are moving toward:

• Docs platform (GitBook, Mintlify, ReadMe, Docusaurus, custom docs site)
• Analytics layer (AI query logs + search analytics)
• Evaluation harness (synthetic questions + retrieval scoring)
• LLM reviewer (agent that proposes edits)

If your docs are mainly API/developer docs, I would prioritize an evaluation pipeline over a generic analytics product: the key question is not just “are AI assistants reading our docs?” but “when an AI assistant answers a developer, did it retrieve the correct section and produce the right implementation guidance?”

I can also suggest a shortlist of tools specifically for API docs teams vs enterprise internal knowledge bases if that’s your use case.

Yes. What you’re describing is essentially AI-search / LLM visibility analytics for technical documentation: which pages AI assistants retrieve or cite, for which questions, where your docs lose to competitors, and what content changes would improve coverage.

A few tools are particularly relevant:

Best fits

1. Mintlify — best if you also want to host/manage the technical docs

Its Assistant provides query and source data, including frequently asked questions, content gaps, unsuccessful interactions, and popular topics. You can export the queries/responses/sources for deeper analysis. mintlify.com
It also has a specific GEO guide for optimizing documentation for ChatGPT, Perplexity, Google AI Overviews, etc. www.mintlify.com

2. Documentation.AI — probably closest to your specific requirement

Its Ask AI Analytics tracks questions, sessions, low-confidence answers, satisfaction, recent questions, and page context. That lets you identify where users are asking questions that your documentation isn't answering well. documentation.ai

3. AI-search/GEO monitoring tools — better if you mean external assistants rather than an AI assistant embedded in your docs.

The important metric here is essentially citation share: give ChatGPT/Claude/Gemini/Perplexity a controlled set of questions and measure:

• Does your company appear?
• Which of your URLs get cited?
• Which competitor URLs get cited instead?
• What claims are being extracted from your pages?
• What questions produce no useful citation?
• Which docs should be rewritten/created?

The distinction I'd make

If your question is:

"Which of our docs are users actually using with our documentation chatbot?"

I'd look at Mintlify or Documentation.AI.

If it's:

"When someone asks ChatGPT/Claude/Perplexity about our product, which of our docs do they cite, and how do we get more of our docs cited?"

That's a different category—AI visibility/GEO analytics. I would not rely on llms.txt alone. For example, Mintlify supports llms.txt/llms-full.txt to make documentation easier for LLMs to consume, but that is primarily an ingestion/discoverability mechanism, not evidence that assistants are actually citing your pages. mintlify.com

If you're evaluating/building a tool internally

I'd want the dashboard to look something like:

And, importantly, it should generate specific recommendations, e.g.:

/docs/authentication/oauth
Cited in 7% of authentication-related AI answers despite ranking highly in your docs search.
Problem: The page explains OAuth conceptually but doesn't explicitly answer "How do I refresh an expired access token?"
Recommendation: Add a dedicated "Refresh an access token" section with request/response examples and link it from the OAuth overview.

That is much more valuable than simply saying "make your headings more AI-friendly."

If you tell me where your technical docs live (GitHub/Markdown, ReadMe, GitBook, Docusaurus, Mintlify, Confluence, etc.), I can recommend the best 3–5 tools for your exact setup, including which ones can actually measure ChatGPT/Claude/Perplexity citations rather than just optimize the docs.

If you mean “which of our technical docs are being used by AI assistants (ChatGPT, Claude, Copilot, Perplexity, etc.) and how can we improve them for AI consumption?”, there are a few categories of tools and approaches. There is not yet a single universal “AI documentation analytics” platform, but these are the closest options:

1. AI visibility / AI search analytics tools

These monitor whether your content appears in AI-generated answers and how competitors compare.

• Profound — Tracks brand visibility in AI answers across systems like ChatGPT, Claude, Gemini, and Perplexity. Useful for seeing which pages are surfaced and where gaps exist.
• Peec AI — AI search monitoring focused on visibility, citations, and competitive analysis.
• Scrunch AI — Helps organizations understand how they appear in AI search results.

These are more marketing-oriented, but they can reveal which documentation pages are being cited or referenced by AI systems.

2. Documentation analytics + AI optimization

If you already have a docs platform, these can help identify high-value content and improve structure.

• Mintlify — Developer documentation platform with analytics and AI-focused documentation generation features.
• ReadMe — API documentation platform with analytics around developer usage.
• GitBook — Documentation platform with AI features and usage insights.

Useful signals:

• pages with high human traffic but poor AI retrieval
• outdated API references
• missing examples
• unclear terminology
• fragmented concepts spread across multiple pages

3. Build your own “AI documentation intelligence” pipeline

Many technical teams are building internal versions because AI usage data is hard to obtain.

A typical stack:

1. Collect documentation signals
   • Docs analytics (page views, searches, failed searches)
   • Git history (stale pages, frequent edits)
   • Support tickets
   • Developer forum questions
   • API logs

2. Run AI evaluation

Use an LLM to score docs for:

• “Can an AI answer common developer questions from this page?”
• “Are examples complete?”
• “Are prerequisites explicit?”
• “Is terminology consistent?”
• “Does this page contain authoritative facts?”

3. Measure retrieval quality

Build a test set:

• 100–500 real user questions
• expected answers
• relevant documentation URLs

Then evaluate:

• Did the AI find the right page?
• Did it hallucinate?
• Was the answer complete?

Tools often used:

• LangSmith
• Arize Phoenix
• Ragas

4. What improvements usually make docs better for AI assistants

AI systems tend to perform better when docs have:

✅ One concept per page
Avoid giant “everything guides.”

✅ Explicit definitions
Bad:

Authentication uses OAuth.

Better:

OAuth is the authorization protocol used by our API. It allows applications to obtain an access token that is sent in the Authorization: Bearer header.

✅ Question-oriented headings
Instead of:

Authentication

Use:

How do I authenticate API requests?

✅ Complete examples
Include:

• request
• response
• errors
• edge cases
• prerequisites

✅ Stable URLs
AI systems rely heavily on indexed references.

✅ Machine-readable structure

• clear Markdown
• tables
• code blocks
• metadata
• version information

If you are producing technical docs, a strong setup is usually:

Docs platform analytics
→ identify important/poorly performing pages
→ AI evaluation harness
→ test against real developer questions
→ AI visibility monitoring
→ measure whether improvements increase citations/retrieval

If you tell me your current stack (for example: GitHub Markdown, Docusaurus, ReadMe, Confluence, GitBook, OpenAPI docs, etc.) and whether you care about ChatGPT citations, internal enterprise copilots, or developer self-service, I can suggest a more specific toolchain.

If your goal is "optimize our technical documentation so AI assistants (ChatGPT, Claude, Gemini, Copilot, etc.) use it more often and answer from it more accurately," there are now several categories of tools. They solve slightly different problems.

1. AI documentation analytics (closest to what you're asking)

These tools analyze your documentation, identify gaps, and often simulate AI retrieval.

• Mintlify
  • AI-powered documentation platform
  • Can identify missing topics, weak pages, broken information architecture, and search issues
  • Includes AI chat over docs, giving insight into what users (and AI) struggle to find

• Inkeep
  • Tracks what AI assistants retrieve
  • Shows unanswered questions
  • Highlights documentation gaps
  • One of the better products if your docs are part of an AI support workflow

• Stainless
  • Focused on API documentation
  • Validates OpenAPI specs
  • Helps generate AI-friendly SDKs and docs
  • Optimizes documentation for developer assistants

2. LLM evaluation tools

These don't analyze writing quality as much as whether an AI can answer questions correctly from your docs.

Examples include:

• Langfuse
• Arize AI
• TruEra

Typical workflow:

1. Ask 500–5,000 realistic developer questions.
2. Retrieve documentation.
3. Have an LLM answer.
4. Measure:
   • retrieval success
   • hallucination rate
   • citation quality
   • answer completeness
5. Report which pages need improvement.

This is often the most actionable approach.

3. Search analytics

If your docs have built-in search:

• Algolia Analytics
• Elastic Search Analytics
• Typesense Analytics

These reveal:

• searches with no results
• searches that end in abandonment
• popular topics
• missing terminology

These signals often align with where AI assistants also struggle.

4. AI crawl/readiness analysis

A newer category focuses on whether AI systems can easily consume your content.

They check things like:

• page structure
• semantic headings
• duplicate content
• canonical URLs
• machine-readable navigation
• API references
• schema markup
• llms.txt support
• chunkability

This is similar to SEO tooling, but optimized for LLM consumption.

5. Build your own evaluation pipeline (recommended for larger doc sets)

Many documentation teams now build automated "AI doc quality" testing.

For each page:

• Generate 20–100 questions users might ask.
• See whether an AI retrieves that page.
• Measure:
  • confidence
  • completeness
  • missing prerequisites
  • ambiguity
  • conflicting pages
• Produce improvement suggestions.

This can be run nightly as part of CI/CD.

What I'd recommend

If you produce a substantial set of developer or technical documentation, a combination tends to work best:

The most effective setups don't just score readability—they test whether real AI assistants can successfully retrieve the right documentation and generate correct answers. That produces concrete recommendations such as adding missing examples, clarifying terminology, restructuring headings, or filling gaps that prevent accurate AI-generated responses.

If your goal is specifically "optimize our documentation so AI assistants (ChatGPT, Claude, Gemini, Copilot, Cursor, etc.) can answer questions from it more accurately," this is an emerging category often called LLM documentation analytics or AI documentation optimization.

The tools fall into a few groups:

For understanding what AI assistants are actually using, newer tools are appearing:

These are closer to an "SEO for AI" approach than documentation quality analysis.

If you want repository-level analysis

If your docs live in GitHub, Confluence, or Markdown repositories, there are AI-powered tools that can analyze the documentation itself:

• Cursor can scan an entire documentation repository and identify inconsistencies, missing examples, and outdated sections.
• Sourcegraph Cody understands code and documentation together and can surface documentation gaps.
• GitBook AI analyzes documentation collections and improves discoverability.

If your goal is "How well can AI answer from our docs?"

This is the capability I'd recommend evaluating. An ideal system should:

• Crawl all documentation.
• Ask hundreds or thousands of representative questions.
• Measure:
  • answer accuracy
  • hallucination rate
  • citation quality
  • missing information
• Identify which pages contribute to successful answers.
• Highlight documentation that is rarely used or never cited.
• Suggest concrete edits such as:
  • adding prerequisites
  • improving headings
  • splitting long pages
  • adding examples
  • filling conceptual gaps
  • improving API parameter descriptions

This is closer to RAG evaluation than traditional documentation analytics.

Enterprise options

If you're building an AI assistant over your own technical documentation, consider:

• LangSmith for evaluating retrieval quality and answer accuracy.
• LlamaCloud for ingestion, indexing, and retrieval evaluation.
• Weights & Biases Weave for LLM application evaluation and tracing.
• Arize Phoenix for observability and RAG evaluation.

These platforms can reveal which documents are retrieved, whether retrieval is correct, and which questions fail due to missing or poorly structured documentation.

For a company with a substantial documentation set (hundreds or thousands of pages), I'd generally combine:

• Inkeep for real user question analytics,
• LangSmith or Arize Phoenix for automated evaluation,
• Profound or Scrunch AI for external AI visibility.

That combination provides insight into both how your internal AI assistant uses your documentation and how public AI assistants reference your content.

If you mean “which parts of our technical documentation are being used by AI assistants (ChatGPT, Claude, Copilot, etc.) and how should we improve them for AI consumption”, there are a few categories of tools. The right choice depends on whether you want usage analytics, AI visibility tracking, or documentation quality optimization.

1. AI search / answer visibility tools (best for “are AIs using our docs?”)

• Scrunch AI — Tracks how brands and content appear in AI-generated answers and helps identify opportunities to improve visibility.
• Profound — Monitors how companies appear in AI search and assistant responses, including competitive analysis.
• Otterly.AI — Tracks mentions and visibility across AI search engines.

These are more focused on AI discoverability than technical documentation specifically, but they can reveal whether your docs are being surfaced.

2. Documentation platforms with AI analytics

If your docs live in a modern documentation platform, look for AI-related analytics:

• Mintlify — Developer documentation platform with AI-friendly docs generation and optimization features.
• ReadMe — API documentation platform with analytics around documentation usage and developer engagement.
• GitBook — Documentation platform with AI-assisted writing and knowledge management features.

These can tell you:

• which pages developers read,
• where users drop off,
• which topics need clarification,
• which docs are likely to become AI retrieval sources.

3. Tools for improving docs for AI retrieval (RAG / assistants)

If your goal is “make our docs better input material for AI assistants”, look at:

• LangSmith — Helps evaluate AI applications and retrieval quality. Useful if you have an internal assistant trained on your docs.
• Arize Phoenix — Observability and evaluation for AI systems, including retrieval performance.
• LlamaIndex — Framework for building and evaluating document-based AI assistants.

These help answer questions like:

• Did the assistant retrieve the correct document?
• Was the answer grounded in our docs?
• Which documents are missing or ambiguous?
• Which chunks are poor retrieval candidates?

4. Build-your-own “AI documentation audit” pipeline

Many technical teams build a lightweight internal system:

1. Crawl docs (Markdown, Confluence, GitBook, PDFs)
2. Split into retrieval chunks
3. Run test questions (“How do I authenticate?”, “How do I handle rate limits?”)
4. Measure:
   • retrieval accuracy,
   • unanswered questions,
   • outdated pages,
   • duplicate/conflicting guidance
5. Generate improvement tickets

Useful components:

• OpenAI API platform for evaluations and assistants
• Pinecone or Weaviate for vector search testing

What I would evaluate for a technical documentation team

A practical stack:

A key distinction: you generally cannot see exactly which private ChatGPT/Claude conversations used your docs. What you can measure is:

• whether your public docs are discoverable by AI systems,
• whether AI crawlers can access them,
• whether retrieval-based assistants answer correctly using them,
• where documentation gaps exist.

If you mean internal enterprise AI assistants (Copilot, ChatGPT Enterprise, Claude Enterprise, etc.) using your private docs, the tool choice is different.

If you mean technical documentation that is consumed by AI assistants (RAG systems, chatbots, coding copilots, support agents, etc.), you’re looking for a mix of documentation analytics + AI retrieval evaluation + content optimization tools. There isn’t one universal “AI assistant doc analyzer,” but these categories cover the problem:

Tools worth evaluating

• LangSmith

Good for teams building RAG assistants. It can trace retrieval steps, show which documents/chunks were used, evaluate responses, and help identify retrieval failures.

• Arize Phoenix

Open-source observability for LLM applications. Useful for seeing retrieved context, hallucinations, and document relevance.

• Langfuse

Open-source LLM analytics platform for tracing prompts, retrieval, and user interactions.

• Weights & Biases Weave

Useful for evaluating and monitoring AI applications.

• Glean

Enterprise search/knowledge assistant platform. Helpful if you want analytics around what internal knowledge sources are being used.

• Google Cloud Vertex AI Evaluation

Provides evaluation workflows for generative AI systems.

For documentation-specific improvement

If your docs are in a documentation platform, look at:

• Document360 — knowledge base analytics and AI features.
• GitBook — docs platform with AI-assisted documentation workflows.
• Confluence — useful if your docs are internal and connected to enterprise AI search.

A practical setup for a technical documentation team

A mature workflow usually looks like:

1. Collect AI queries
   • Questions users ask your assistant/copilot
   • Support tickets
   • Search logs

2. Measure retrieval
   • Which docs/chunks were retrieved?
   • Were the right docs selected?
   • Were answers cited correctly?

3. Score gaps
   • “The API authentication docs were retrieved but missing OAuth examples”
   • “Users ask about migration but no migration guide exists”
   • “Terminology differs between docs and user questions”

4. Create improvement backlog
   • Add missing examples
   • Improve headings and metadata
   • Split overly long pages
   • Add canonical answers for common AI queries

If you already have an AI assistant connected to your docs, the best first tool depends on what stack you use (for example: GitHub + Markdown, Confluence, GitBook, ReadMe, Docusaurus, Zendesk, or a custom RAG system). I can suggest a more targeted setup if you share your documentation platform and whether you control the AI assistant.

If your goal is to optimize technical documentation so AI assistants (ChatGPT, Claude, Gemini, Copilot, etc.) can retrieve and answer from it more effectively, there are a few categories of tools. No single tool perfectly tells you "this document is used by AI assistants," but several can analyze AI readiness and identify gaps.

Here are the strongest options:

For AI-specific optimization, these are especially useful:

1. Inkeep (probably closest to what you're asking)

If you have an AI assistant over your docs, Inkeep provides insights such as:

• Questions AI couldn't answer
• Documents frequently retrieved
• Missing documentation
• Hallucination hotspots
• Suggested new articles

This is one of the few products designed specifically around AI documentation quality.

2. Mintlify

Mintlify's documentation platform includes:

• AI readability analysis
• Documentation completeness
• SEO and LLM optimization
• Detection of weak pages
• Recommendations for restructuring

3. Build your own "AI doc evaluator"

Many documentation teams create an evaluation pipeline using an LLM. For each page, they automatically score things like:

• Can an AI answer questions from this page?
• Is terminology defined?
• Are prerequisites clear?
• Are examples complete?
• Is the page self-contained?
• Could another page be merged or split?
• Would retrieval likely confuse this with another document?

This often produces a scorecard such as:

Many companies automate this for thousands of documents.

4. Measure retrieval directly

The most effective approach is to evaluate your documentation as an AI system would:

1. Generate hundreds or thousands of realistic user questions.
2. Run retrieval against your documentation.
3. Check whether the correct document is retrieved.
4. Have an LLM grade the answer for accuracy and completeness.
5. Report which documents consistently fail.

This identifies documents that are difficult for AI assistants to use, even if they read well to humans.

If you're producing developer documentation

A strong modern stack is:

• Docs platform: Mintlify or ReadMe
• Analytics: Inkeep
• Search: Algolia
• Evaluation: An automated LLM-based evaluation pipeline (using models from providers such as OpenAI or Anthropic) that regularly scores documentation quality and retrieval performance.

That combination gives you both usage insights and actionable recommendations for improving documentation so AI assistants can answer accurately from it.