ChatGPT answers, unedited

If your goal is specifically “how often do AI assistants cite our technical docs as sources?”, I’d look first at Profound.

It tracks:

• Which URLs AI systems cite, including individual documentation pages—not just whether your brand is mentioned. www.tryprofound.comhelp.tryprofound.com
• Citation frequency / citation share over time.
• Performance across ChatGPT, Claude, Gemini, Perplexity, Google AI Overviews/AI Mode, Copilot, etc. www.tryprofound.com
• Top Citation Pages, so you can identify which specific technical docs are being used most.
• Watched Pages, which is particularly useful if you want to monitor a defined set of /docs/, /developers/, or /api/ URLs. www.tryprofound.comhelp.tryprofound.com
• An API for pulling citation data into your own dashboards. docs.tryprofound.com

Another strong option

OtterlyAI is worth evaluating too. Its prompt monitoring explicitly reports Domain Citations, including cumulative citation counts, and lets you drill down to response- and citation-level detail. help.otterly.ai

The important distinction

If you're a B2B developer/technical company, I'd structure the measurement around docs URLs rather than brand mentions:

“Of the AI answers to questions about X, how many cited docs.mycompany.com/...?”

Then track:

1. Citation rate — % of relevant AI answers citing your docs
2. Citation count — number of citations
3. Unique docs cited — which technical pages are actually useful to models
4. Citation by assistant — ChatGPT vs Claude vs Gemini vs Perplexity
5. Citation by topic/query — e.g. authentication, API limits, SDK usage
6. Citation trend — whether documentation changes increase/decrease usage

For this particular use case, Profound looks like the better fit because its citation model explicitly supports owned domains, help centers, and documentation sites, and its Watched Pages feature lets you monitor individual URLs. www.tryprofound.comhelp.tryprofound.com

One caveat: these tools generally measure citations in monitored AI responses, not literally every time an underlying model internally retrieves or “reads” your documentation. That's an important distinction if you're trying to measure actual AI-agent traffic rather than AI-search visibility.

Yes. What you’re looking for is generally called AI visibility / AI citation tracking—but for a B2B company with a large technical-docs site, I’d specifically look for a tool that tracks URL-level citations, not just brand mentions.

A few good options:

• ahrefs.com — particularly interesting for your use case because it distinguishes between pages AI actually cites and pages it merely found/retrieved. That lets you measure whether specific docs are becoming sources in AI answers. Ahrefs Help Center
• surferseo.com — tracks which domains/pages are cited for a set of prompts and how frequently they appear. Surfer SEO Docs
• citations.io — monitors citations across ChatGPT, Gemini, Perplexity, and Claude, including individual citation activity and prompts. Citations.io
• mentionflow.ai — has a dedicated sources/citations view that ranks the domains and pages AI engines cite across your tracked prompts. MentionFlow
• bing.com — worth using even if you buy another tool. It reports how often your pages are cited in Copilot/Bing AI experiences and exposes the grounding queries associated with your content. Search - Microsoft Bing

For a technical-docs-heavy B2B company

I'd prioritize Ahrefs Brand Radar or MentionFlow and configure prompts around questions such as:

"How do I authenticate with [product]?"
"What's the API rate limit for [product]?"
"Compare [your product] vs [competitor]."
"How do I implement [technical task] using [your technology]?"

Then track, per prompt:

• Whether your domain was cited
• Which exact documentation URL was cited
• Citation frequency over time
• Which competing docs were cited instead
• Which AI engine/model produced the citation
• Which prompts produce citations to your docs
• Which docs are retrieved but not actually cited, if the tool exposes that distinction

That last metric is particularly valuable. A page can be discovered by an AI system without appearing as a source in the final answer; Ahrefs explicitly separates those two states. Ahrefs Help Center

One caveat: these tools generally don't tell you the absolute number of times real users asked AI assistants about your product and your docs were used. They usually run a controlled panel of prompts repeatedly and measure the resulting citations. That's a measurement of AI-source visibility, rather than actual total usage.

If you tell me roughly how many docs/pages you have and which assistants matter most (ChatGPT, Claude, Gemini, Perplexity, Copilot), I can narrow this to the 2–3 tools that fit a technical B2B use case best.

Yes. For that specific use case, I’d look at AI-search visibility / citation-monitoring tools, rather than traditional SEO tools.

Best fit: Scrunch

scrunch.com is particularly aligned with what you’re describing. It monitors AI responses to a set of prompts and records the URLs cited by the AI assistant, including your own technical documentation. You can drill down to individual URLs and see:

• How many prompts cited a documentation page
• How many AI responses cited it
• Citation consistency — the percentage of responses citing that source
• Which AI platforms cited it
• Which specific prompts produced the citations
• Citation trends over time
• Your pages vs. competitor/third-party sources Scrunch Help Center Scrunch

That makes it possible to answer something like:

“Our API authentication guide was cited in 18% of tracked ChatGPT responses to questions about OAuth, versus 3% for our competitor's documentation.”

Scrunch also supports ChatGPT, Perplexity, Gemini, Claude, Google AI Overviews/AI Mode, Copilot, Grok, and Meta AI. Scrunch

ai-cdn.scrunchai.com

One important caveat

These products don't literally measure every AI assistant response on the internet. They generally run/monitor a representative set of prompts and observe the citations in the resulting responses. So the useful metric is citation rate across a controlled prompt set, not “AI used our docs X times globally.”

For a B2B company with technical docs, I'd structure the monitoring around:

1. Product/category questions — “best API platform for…”
2. Technical questions — “how do I implement X with…”
3. Integration questions — “X API + Salesforce”
4. Troubleshooting questions
5. Comparison questions — “X vs Y”
6. Your highest-value docs URLs

Then track citation rate per documentation page × prompt topic × AI platform.

If your actual goal is “How often are our docs being used as authoritative sources by ChatGPT/Claude/Perplexity, and which docs are winning?”, Scrunch is probably the first tool I'd evaluate. Scrunch

Yes. What you’re looking for is generally called AI citation tracking / AI search visibility / AEO monitoring.

Two particularly relevant tools:

• Scrunch — probably the closest match. It tracks the exact URLs cited by AI assistants, including your documentation/help-center pages, and reports citation frequency, citation consistency, prompts, platform, and trends. You can drill down to individual URLs. helpcenter.scrunchai.com
• Profound — another strong option. Its Citation Tracking shows which answer engines cite your content, how often, and across which prompts. It explicitly categorizes documentation/help-center sites as owned sources. www.tryprofound.com

For a B2B technical-docs team

I'd evaluate Scrunch first if your core question is:

“How many times did ChatGPT/Perplexity/etc. cite this specific documentation page over the last 30/90 days?”

Scrunch exposes metrics at the URL level, including the number of prompts and responses citing a particular source, plus citation consistency and an influence score. ai-cdn.scrunchai.com

Profound is attractive if you also want broader AI visibility/share-of-voice and competitive benchmarking alongside citations. www.tryprofound.com

One important caveat: these tools generally sample/query AI assistants with tracked prompts; they cannot literally observe every private ChatGPT/Claude/Gemini conversation in the world. So the useful metric is usually “citation rate across a representative prompt set,” not absolute global usage.

If you tell me your stack (e.g. Mintlify/Docusaurus/GitBook/ReadMe + ChatGPT/Claude/Gemini) and roughly how many docs pages you have, I can recommend the best tool and setup.

Yes. What you’re looking for is generally called AI citation tracking or answer-engine visibility (AEO) analytics.

For a B2B company with technical docs, I’d look at these first:

1. Scrunch — probably the closest fit
   • Tracks the exact URLs AI assistants cite in responses.
   • Lets you drill down to individual documentation URLs and see how many prompts/responses cited each one, citation frequency, and trends over time.
   • Covers ChatGPT, Claude, Perplexity, Google AI Overviews/Mode, Gemini, Copilot, Grok, and Meta AI. scrunch.com
   • You can specifically monitor your docs/help-center URLs rather than just measuring whether your company name was mentioned. scrunch.com

2. Profound — stronger enterprise/AEO analytics
   • Tracks citation frequency and citation share versus competitors.
   • Its citation categorization explicitly supports documentation sites/help centers as “Owned” sources, which is useful for a technical-docs strategy. www.tryprofound.com
   • Good if you want to answer questions like “What percentage of AI citations in our category come from our docs vs. competitors' docs?”

The metric I'd actually track

For your use case, I wouldn't just measure "AI visibility." I'd build a dashboard around:

Docs citation rate

% of monitored AI responses that cite at least one URL from docs.yourcompany.com

Then break it down by:

• AI assistant: ChatGPT / Claude / Gemini / Perplexity / Copilot
• Documentation URL
• Product
• Topic / technical question
• Prompt type: troubleshooting, implementation, comparison, API usage, etc.
• Competitor citations
• Citation frequency over time

For example:

Scrunch looks particularly well suited to this exact question because it records the cited URL and lets you see the prompts and number of responses associated with that URL. scrunch.com

One important caveat: these tools generally measure citations in monitored AI responses, not literally every time an AI model internally retrieves or reads your documentation. Those are different things. Scrunch itself distinguishes citation tracking from AI-agent/bot traffic. helpcenter.scrunchai.com

If you tell me what kind of B2B company you are (developer API, cybersecurity, SaaS, infrastructure, etc.) and where your docs live (ReadMe, Mintlify, GitBook, custom, etc.), I can recommend the best tool and setup for measuring this.

For a B2B company, you’re looking for an AI citation tracking / AI visibility platform (sometimes called GEO — Generative Engine Optimization tooling). These tools monitor whether AI assistants cite your docs, help pages, API references, blogs, or other web content when answering customer questions. citations.io

Some options:

For technical documentation specifically (API docs, SDK docs, developer guides), I’d evaluate tools based on whether they can answer:

• Which exact doc URLs are being cited?
• Which AI assistants/models cite them? (ChatGPT, Claude, Gemini, Perplexity, Copilot)
• For which customer questions?
• Are competitors’ docs being cited instead?
• Are docs being used but not credited with a citation? (harder to measure)

A common setup for a B2B SaaS company is:

1. Pick 50–200 real buyer/developer questions (“How do I authenticate?”, “Does product X support SSO?”, “API rate limits?”).
2. Run those prompts regularly across AI assistants.
3. Store the responses and extract cited URLs.
4. Track metrics like:
   • documentation citation rate
   • top cited docs pages
   • competitor citation share
   • unanswered questions where AI gives incomplete answers

If your docs are the main asset you care about, a generic “brand mention” tracker may not be enough—you’ll want URL-level citation tracking and ideally API/export access for analysis. llmpulse.ai

Yes — the category you’re looking for is AI search / LLM visibility monitoring, and one particularly relevant tool is Scrunch.

For a B2B company with technical documentation, Scrunch can track:

• Which exact URLs from your docs are cited in AI-generated answers.
• How frequently each URL is cited, including citation consistency over monitored prompts. helpcenter.scrunchai.comscrunch.com
• Which AI platforms are citing them — including ChatGPT, Claude, Gemini, Perplexity, Google AI Overviews, and Copilot. helpcenter.scrunchai.com
• Which prompts produce those citations, so you can see what questions cause your docs to surface.
• Citation trends over time and comparisons against competitors.
• Your citation rate — the percentage of monitored prompts where your brand receives at least one citation. helpcenter.scrunchai.com

For your specific use case, I'd set up prompts around things like:

"How do I authenticate with [product]?"
"How does [product] handle webhooks?"
"Best APIs for [use case]"
"[technical problem] solutions"

Then monitor whether the AI response cites docs.yourcompany.com/... and which individual pages are repeatedly used.

Important distinction: these tools generally measure observed citations from a controlled set of prompts, not the total number of times all users everywhere asked ChatGPT/Claude/etc. questions and your docs happened to be used internally. That's an inherently difficult number to observe.

If your goal is specifically "Which pages in our technical docs are becoming sources for AI assistants, and how often?", Scrunch's Citations functionality is unusually close to exactly what you're describing. helpcenter.scrunchai.comscrunch.com

If you want, I can also compare Scrunch vs Profound vs Otterly vs Peec AI specifically for technical-doc citation tracking, including which one gives you the best page-level data and API/export capabilities.

Yes. What you’re looking for is generally called AI visibility / LLM visibility / AI citation tracking.

For a B2B company with a substantial technical-docs site, I’d look at these:

• Microsoft Clarity AI Visibility — particularly interesting because its Citation dashboard shows which pages on your domain are cited, how often, and the queries associated with those citations. It also reports AI referral traffic. learn.microsoft.com
• DemandSphere LLM Tracking — more enterprise-oriented; tracks citations and visibility across ChatGPT, Gemini, Perplexity, Copilot and other AI platforms. Its API can expose the specific cited URL, citation position, prompt, surrounding context, and AI platform. www.demandsphere.com
• Webflow Prompt Insights — useful if your docs/content are already in Webflow. You specify customer questions and it checks ChatGPT, Claude, Gemini and Perplexity, showing which domains get cited. help.webflow.com
• AIclicks — broader GEO/AEO monitoring with citation-source tracking across ChatGPT, Perplexity, Gemini, Claude, Copilot, AI Overviews and others. docs.aiclicks.io
• LLM Pulse — specifically useful for analyzing which URLs and pages AI models cite, with citation rate and per-model breakdowns. llmpulse.ai

One important distinction

There are really two different things you might want to measure:

1. "Does AI cite our docs?"

→ Citation/LLM visibility tools above are designed for this. They generally run a controlled set of prompts against AI systems and record the sources returned.

2. "How often are AI assistants actually crawling/using our docs?"

→ That's different. You want AI crawler/bot analytics (e.g., GPTBot, ClaudeBot, Google-Extended, PerplexityBot), server logs, and potentially analytics that distinguish AI referral traffic. Microsoft specifically separates citation activity from bot activity. learn.microsoft.com

For a technical B2B docs site, I'd ideally combine both: crawler activity + actual citation frequency + the exact docs URLs being cited. That lets you answer something much more useful than "our AI visibility score went up":

"Our API authentication docs were cited 184 times this month across ChatGPT, Gemini and Perplexity, up 37%; these five pages account for 72% of citations."

If you tell me roughly what kind of B2B company you are (developer tool/SaaS/infrastructure/etc.) and where the docs live (GitBook, ReadMe, Mintlify, Docusaurus, custom, etc.), I can narrow this down to the 2–3 tools that are actually best suited to tracking technical documentation citations, rather than generic brand visibility.

Yes. The category you’re looking for is AI visibility / AI citation tracking (sometimes called GEO/AEO tooling).

For a B2B company with technical docs, I’d look at these:

• Microsoft Clarity AI Visibility — particularly interesting because its Citation dashboard reports which pages on your domain are cited, how often, and the queries associated with those citations. It also distinguishes citation data from AI-bot crawling activity. learn.microsoft.com
• AIclicks — tracks citations across ChatGPT, Perplexity, Gemini, Claude and others, and connects citations back to their source URLs. docs.aiclicks.io
• Citations.io — focuses specifically on which sources AI assistants cite, with prompt-level and competitor comparisons across ChatGPT, Gemini, Perplexity and Claude. citations.io
• ScalePost — different approach: watches actual AI-agent requests at the CDN layer, rather than repeatedly querying AI assistants. That's useful if your question is literally “Are AI agents fetching our docs?” rather than “Does our documentation appear as a cited source in answers?” www.scalepost.ai

The important distinction

There are actually two metrics you may want:

For a technical B2B company, I'd prioritize page-level + prompt-level citation tracking. A simple brand-visibility score isn't enough—you want something like:

/docs/api/authentication → cited 37 times → ChatGPT 18, Claude 11, Perplexity 8 → primarily for “how do I authenticate with X API?”

That's much more actionable for a documentation/content team.

One caveat: AI citation trackers generally measure sampled/generated answers, not every response that every user receives. Microsoft Clarity's newer citation product is notable because it claims query-level citation data rather than simply estimating visibility. learn.microsoft.com

If you tell me what kind of B2B company you are (e.g. API/developer tool, SaaS, cybersecurity, infrastructure), I can narrow this to the 3 best tools specifically for tracking citations of technical documentation, including pricing and API/integration options.

Yes. What you’re looking for is generally called AI visibility / AI citation tracking or GEO (Generative Engine Optimization) software.

For a B2B company with lots of technical documentation, I’d shortlist:

• Scrunch — strong enterprise-oriented option. It tracks citations across multiple AI engines and can break down which sources/pages are being cited. A 2026 comparison lists it as particularly suited to enterprise teams. paylinedata.com
• Citely — particularly interesting if you want an audit trail. It says it records individual citations, lets you drill into the exact answer/source, and can export citation data. www.citely.tech
• MyMentions — explicitly surfaces source-level citations, including owned product docs/help centers, and supports multiple AI providers. www.mymentions.org
• Microsoft Clarity AI Visibility — interesting if you want to understand not only citations but also which AI bots crawl your docs and how frequently. It combines citation data with CDN-level bot activity. clarity.microsoft.com
• LLM Pulse — useful for specifically analyzing which domains, hosts, and individual pages AI systems cite, with citation rates and per-model breakdowns. llmpulse.ai

For your particular use case

I'd define the metric as:

Technical-doc citation rate = number of AI answers citing a URL in our docs ÷ number of tracked prompts where our docs could plausibly be relevant.

Then track it by:

AI assistant → prompt → cited URL → documentation section/page → citation frequency over time

That's more useful than simply measuring whether your company is mentioned. For example, you might discover that docs.example.com/api/authentication is cited 37 times/month by ChatGPT and Perplexity, while your marketing site is never cited.

One important caveat: these tools generally probe AI systems with predefined prompts and measure the resulting citations. They cannot necessarily tell you the absolute number of times all users everywhere saw your docs cited. Some products also offer actual bot/crawler telemetry, which is a different metric. clarity.microsoft.com

If you're specifically trying to measure “how often are our developer docs cited by ChatGPT/Claude/Gemini/Perplexity?”, I can compare the best 5 tools specifically on technical-doc/page-level citation tracking, API access, historical data, and pricing.

Yes. What you’re looking for is usually called AI citation tracking, AI search visibility, or GEO (Generative Engine Optimization) analytics.

For a B2B company with technical docs, I’d look first at:

• OtterlyAI — probably the most direct fit. You define prompts such as “how do I integrate X with Y?” and it monitors AI engines daily. It reports which URLs were cited and how many times, including your specific documentation URLs, not just whether your brand was mentioned. help.otterly.ai
• Peec AI — particularly good for understanding source visibility: which domains AI systems retrieve/cite, citation rate, and retrieval rate. It distinguishes between being mentioned as a brand and actually being used as a source. docs.peec.ai
• Profound — worth considering if you're an enterprise B2B company wanting broader AI-search intelligence and reporting.

The important distinction for technical docs

You probably don't just want:

“Does ChatGPT mention Acme?”

You want:

“When someone asks an AI assistant a question our documentation could answer, how often does it retrieve or cite docs.acme.com, which pages does it cite, and which questions generate those citations?”

That's a more useful metric for a developer-docs team.

For example, you could track:

That last distinction matters: being retrieved isn't necessarily the same as being cited. Research and current AI-visibility tooling increasingly treat those as separate signals. docs.peec.ai

One caveat: these platforms generally sample prompts and AI responses rather than giving you a magical counter of every AI assistant response in the wild. AI answers are nondeterministic, so a good system should run enough repeated observations to make the citation-rate estimate meaningful. arxiv.org

If your specific goal is “which pages in our developer docs are being cited by ChatGPT/Claude/Perplexity, and how often?” I'd start with OtterlyAI and Peec AI and compare them.

Yes. What you’re looking for is generally called AI visibility / GEO (Generative Engine Optimization) tracking—specifically citation tracking at the URL/page level, rather than just tracking whether your brand is mentioned.

A few tools worth evaluating:

• Citations.io — probably the closest fit to your question. It runs customer-relevant prompts against ChatGPT, Gemini, Perplexity, and Claude, then shows which sources/URLs those systems cite, including your competitors. citations.io
• IndexMind — explicitly tracks AI citations and distinguishes direct citations from mere brand mentions, which is useful if you want to know whether your technical docs themselves are being sourced. www.indexmind.ai
• Ahrefs AI Visibility / Brand Radar — useful if you already use Ahrefs and want AI visibility alongside conventional SEO. Its AI Visibility Checker covers ChatGPT, Gemini, Perplexity, Copilot, and Google AI Overviews. ahrefs.com
• Profound, Peec AI, Otterly, AthenaHQ — other dedicated AI-search visibility platforms that track mentions, citations, prompts, and competitors. Community comparisons suggest these are among the more commonly evaluated options, although I'd validate their exact technical-document/URL-level reporting before buying. www.reddit.com

The important distinction for a B2B technical-docs team

I'd look for a tool that can answer:

“Of the AI answers to questions about our product/API, how many cited docs.example.com/... and which documentation pages were cited?”

Not merely:

“How often was ExampleCo mentioned?”

The metrics I'd want are:

1. Citation rate — % of tracked AI answers citing your docs.
2. Citations by URL — which individual documentation pages get cited.
3. Citation frequency over time.
4. AI engine/model — ChatGPT vs. Claude vs. Gemini vs. Perplexity, etc.
5. Prompt/query that produced the citation.
6. Competitor/source substitution — which docs or third-party sources get cited instead.
7. Accuracy — whether the AI actually represented the documentation correctly.
8. Coverage of technical questions — especially API, integration, troubleshooting, and “how do I…” queries.

One caveat: these numbers aren't equivalent to server-side analytics. AI responses are stochastic, and research published in 2026 found substantial variation in which sources get cited across repeated runs. So a good system should sample prompts repeatedly and report trends, rather than claiming an exact universal “% of AI responses” figure. arxiv.org

If you tell me roughly how many docs/pages you have and which AI assistants matter most (ChatGPT, Claude, Gemini, Perplexity, etc.), I can narrow this down to the 2–3 tools I'd actually shortlist for a B2B developer-docs use case.

Yes. What you’re looking for is generally called AI citation tracking / AI visibility / AEO (answer-engine optimization).

For a B2B company with a large technical-docs site, I’d look at:

• Surva.ai — probably the closest match to your specific question. It tracks the exact URLs cited by ChatGPT, Perplexity, Claude, Gemini, and Google AI Overviews, including which content gets referenced and the position of the citation. www.surva.ai
• Citations.io — focused specifically on AI citations, with a feed of cited URLs, citation frequency, competitors, and prompt-level tracking across ChatGPT, Gemini, Perplexity, and Claude. citations.io
• TrackCited — broader AI-visibility monitoring; useful for seeing which sources AI assistants cite and where competitors are winning. www.trackcited.com
• Ahrefs Brand Radar — worth considering if you're already an Ahrefs customer. It tracks AI visibility across multiple engines and identifies the URLs being cited. www.techradar.com

One important distinction

If your question is literally:

“How many times did an AI assistant use this particular documentation page as a source?”

these tools generally don't measure every real-world AI answer generated on the internet. They run a defined set of prompts against AI engines and record the citations they observe. That's an important methodological limitation. oversearch.ai

For technical docs, I'd therefore want a tool that gives you a dataset roughly like:

Surva.ai is the one I'd investigate first for that use case because its documentation explicitly describes URL-level citation tracking and filtering by platform/time period. www.surva.ai

If you're actually trying to build an internal analytics system that measures which of your docs are being retrieved/cited by AI agents, rather than an SEO/AEO marketing tool, that's a somewhat different problem—and I can outline the architecture for that.

Yes. What you’re looking for is usually called AI citation tracking, AI visibility monitoring, or GEO/AEO monitoring.

For a B2B company with a large technical-docs site, I’d shortlist:

• Microsoft Clarity AI Visibility — particularly interesting because its citation dashboard shows which pages from your domain are cited, citation counts, and the queries associated with those citations. learn.microsoft.com
• Surva.ai — explicitly tracks citations at the URL level, including frequency, platform, and time period. www.surva.ai
• Orchly — has a source-level view showing which pages AI engines cite, how often, and for which prompts. orchly.aiflowcourier.com
• PromptScout — tracks the sites/pages AI systems cite and lets you inspect source patterns across AI answers. www.promptscout.app

For technical documentation specifically

I'd distinguish two different things you may want to measure:

1. Citation rate:

“When an AI assistant answers questions about our product, how often does it cite docs.acme.com?”

2. Individual-doc usage:

“Which 37 documentation pages are being cited, and how many times has each been cited?”

The second is much more useful for a technical-content team. You can identify things like:

Microsoft Clarity's current AI Visibility product is particularly close to this use case because it exposes cited pages and the associated grounding queries, rather than giving you only a generic “AI visibility” score. learn.microsoft.com

One important caveat: server-side AI crawler analytics aren't the same thing as citation tracking. Seeing GPTBot or ClaudeBot fetch your docs proves that an AI system accessed the page, but it doesn't prove that the page appeared as a source in the resulting answer. Research also finds that retrieval and visible citation can diverge. flowcourier.com

If you tell me roughly how many docs/pages you have and which assistants matter (ChatGPT, Claude, Gemini, Perplexity, Copilot, etc.), I can recommend the 3 best tools for a B2B technical-docs team and compare pricing, coverage, and API/export capabilities.

If you mean tracking when AI assistants (ChatGPT, Claude, Gemini, Perplexity, etc.) cite or rely on your B2B company’s technical documentation, you’re looking for an AI visibility / AI search analytics / LLM observability tool rather than traditional web analytics.

Tools in this category include:

• Profound — Tracks brand visibility in AI-generated answers, including whether AI systems mention your company and how they represent it.
• Scrunch AI — Focuses on monitoring how brands appear across AI assistants and improving content discoverability.
• Otterly.AI — Tracks prompts and AI-generated search results to measure visibility in AI engines.
• Peec AI — Monitors how brands appear in ChatGPT and other AI answers for target queries.
• Writesonic — Includes AI search visibility features alongside content optimization.

For technical documentation specifically, you may also combine AI visibility tools with:

• Documentation analytics (to see human traffic and engagement):
  • Google Analytics
  • Matomo Analytics
• Log-based analysis (if your docs are behind an API/CDN): analyze crawler traffic, referrers, and access patterns.
• Custom LLM evaluation pipelines: run a set of realistic customer questions against major AI assistants on a schedule, then measure:
  • Did the answer mention your docs?
  • Was your documentation cited?
  • Which pages were referenced?
  • Were competitors cited instead?
  • Was the answer accurate?

A typical B2B technical-docs setup looks like:

1. Create a prompt set (e.g., “How do I configure SSO with Product X?”).
2. Run those prompts weekly/monthly across AI assistants.
3. Capture citations and sources.
4. Score:
   • citation share
   • source quality
   • accuracy
   • competitor mentions
   • missing documentation gaps

One important caveat: there is no universal API that tells you “ChatGPT used your docs X times this month.” AI assistants generally do not expose that private usage data. The available tools estimate visibility by testing prompts and monitoring public AI outputs.

If your company has a developer portal/API docs/knowledge base, the most useful approach is usually an AI visibility tracker + automated citation monitoring + your own docs analytics rather than relying on a single tool.

Yes. The category you’re looking for is AI visibility / AI citation tracking (sometimes called AEO/GEO tracking).

For a B2B company with a large technical-docs site, I’d look at these:

• Microsoft Clarity AI Visibility — probably the most directly relevant starting point. Its Citation dashboard shows which pages on your domain are cited in AI-generated answers, citation counts, and the queries associated with those citations. learn.microsoft.com

Microsoft Clarity AI Visibility

• Citations.io — purpose-built AI citation monitoring across ChatGPT, Gemini, Perplexity, and Claude. It lets you see which sources/URLs are cited and compare citation share against competitors. citations.io

Citations.io

• LLM Visibility — tracks prompts across several AI engines and specifically exposes the sources feeding the models, alongside brand visibility and sentiment. llmvisibility.tech

LLM Visibility

For technical docs specifically

I'd distinguish between:

1. "Does AI cite our docs?"
→ Clarity's citation dashboard is unusually close to this use case.

2. "Which AI assistants cite which documentation pages, for which technical questions?"
→ A dedicated AI visibility tracker such as Citations.io/LLM Visibility is likely more useful.

3. "How frequently are AI crawlers actually fetching our docs?"
→ That's a different measurement problem. Server/CDN logs and bot analytics can tell you about crawler activity, but a crawler visiting a page does not mean the page was subsequently cited in an answer.

One important caveat: AI responses are stochastic, so a metric like "our docs were cited 17% of the time" should be based on repeated sampling rather than a handful of prompts. Recent research finds substantial variation in citation results across repeated runs. arxiv.org

If you're evaluating this as a B2B product category, I'd specifically look for a tool that can report:

AI engine → prompt/topic → cited URL → citation frequency → competitor/source cited instead → date

That's much more actionable for a technical-content team than a generic "AI visibility score."

If you tell me roughly how many docs/pages you have and which assistants matter (ChatGPT, Claude, Gemini, Perplexity, etc.), I can narrow this down to the 2–3 best tools for your use case.

If your goal is measuring how often AI assistants (ChatGPT, Claude, Gemini, Perplexity, Copilot, etc.) cite or rely on your documentation, there isn't yet a single universal analytics platform because most AI assistants don't expose referral logs. Instead, companies typically combine a few types of tools.

The strongest options are:

For a B2B SaaS company with technical documentation, I'd also recommend instrumenting your docs themselves:

• Add server-side analytics to every documentation page.
• Track requests from AI crawlers such as:
  • OpenAI's GPTBot
  • Anthropic's ClaudeBot
  • PerplexityBot
  • Google-Extended
  • Bytespider (where relevant)
• Monitor whether llms.txt and structured documentation are being fetched.
• Use log analysis (Cloudflare, Fastly, Datadog, etc.) to see which documentation pages AI crawlers access most frequently.

It's important to distinguish two different metrics:

1. Crawler activity ("AI models read this page")
   • Measured from your web logs.
   • Doesn't prove the content was later used in responses.

2. Citation activity ("AI assistants referenced this page in an answer")
   • Measured by platforms like Profound or Scrunch AI.
   • This is the closest available metric to "our docs influenced AI answers."

If you're a developer platform

The most useful KPI dashboard usually includes:

• % of benchmark prompts where your company is mentioned
• % where your documentation is cited
• Which documentation pages are cited most
• Missing topics competitors are cited for
• Citation trend over time
• Share of voice versus competitors
• AI crawler traffic to docs

This combination gives both leading indicators (crawler access) and outcome metrics (actual AI citations).

If you're evaluating vendors today, Profound is generally regarded as the most mature enterprise platform for AI answer visibility, while Scrunch AI is particularly strong for technical documentation and developer-focused content.

If your goal is specifically to understand how often AI assistants (ChatGPT, Claude, Gemini, Perplexity, etc.) use your technical documentation as a source, there are a few emerging categories of tools. None provide perfect visibility, because AI assistants don't expose complete citation telemetry, but some are much better than traditional analytics.

Here are the strongest options:

For developer documentation, I'd prioritize:

1. Profound AI – strongest if you want executive-level reporting on how your docs influence AI responses.
2. Scrunch AI – particularly focused on SaaS companies with technical documentation.
3. Goodie – useful if your main concern is generative search and documentation discoverability.

If you want first-party measurement

If your docs are hosted on your own site (for example, Docusaurus, Mintlify, ReadMe, or a custom docs portal), you can also measure AI usage directly by combining:

• Web analytics (GA4, Plausible, PostHog)
• Server logs
• AI crawler detection (OpenAI, Anthropic, Google, Perplexity, etc.)
• Referral traffic from AI products
• Structured documentation sitemaps (LLMs.txt and related metadata where supported)

This tells you:

• Which documentation pages AI crawlers access
• Which pages receive visits originating from AI assistants
• Which pages are most likely to be used as grounding material

If you're building a serious AI documentation strategy

Many B2B companies now use a combination of:

• An AI visibility platform (Profound or Scrunch)
• Product analytics (PostHog or Amplitude)
• Search Console
• Traditional SEO tools (Ahrefs or Semrush)
• Documentation analytics (ReadMe, Mintlify Analytics, etc.)

That combination gives both "Was our documentation cited by AI?" and "Did that lead to users visiting or adopting our product?"

If your company publishes API or SDK documentation, I can also recommend tools specifically optimized for tracking citations of developer docs rather than general marketing content.

If your goal is specifically measuring how often AI assistants (ChatGPT, Claude, Gemini, Perplexity, etc.) use your documentation as a source, you're looking for an emerging category often called AI visibility, LLM analytics, or Generative Engine Optimization (GEO).

Some of the better-known tools include:

If your company publishes technical documentation

The most useful metrics are usually:

• How often your docs are cited by AI assistants
• Which pages are cited most frequently
• Which competitors are cited instead
• The prompts that surface your documentation
• Citation share by model (ChatGPT vs Claude vs Gemini vs Perplexity)
• Changes in visibility over time

Some enterprise platforms also identify which exact documentation URL was referenced and whether the model quoted it, summarized it, or simply relied on it as a source.

If you're a developer company

If you have API docs or SDK documentation, I'd prioritize:

1. Profound
2. Scrunch AI
3. Peec AI

These tend to focus more on documentation quality, structured content, and technical knowledge rather than only marketing pages.

One important limitation: no external tool can directly observe every internal retrieval or training source used by an AI model. These platforms estimate usage by systematically querying multiple AI assistants at scale, recording responses, detecting citations or referenced URLs, and tracking trends over time. That makes them very useful for competitive benchmarking and optimization, but they don't provide a complete count of every time an AI model relied on your documentation.

If your objective is product analytics (e.g., "How many AI agents are actually reading our docs from our website?"), there are also observability tools that analyze crawler traffic, AI user agents, and retrieval requests—a somewhat different problem from tracking citations in AI-generated answers.

If your goal is specifically "How often do AI assistants (ChatGPT, Claude, Gemini, Perplexity, etc.) use our documentation as sources?", you're looking for an emerging category often called AI visibility, LLM analytics, or Generative Engine Optimization (GEO).

Here are the leading options:

If you have technical documentation

For companies with docs hosted on sites like:

• docs.company.com
• developer.company.com
• support.company.com

the better platforms will report metrics such as:

• How often your docs are cited
• Which pages are cited most
• Which AI assistants cite them
• Which prompts trigger citations
• Competitors cited instead of you
• Citation trends over time

This is similar to SEO tools like Ahrefs or Semrush, but for AI assistants.

If you want first-party measurement

Many companies also combine these tools with:

• Web analytics (GA4, Amplitude, Mixpanel)
• Server logs
• Referrer analysis
• AI crawler monitoring (e.g. OpenAI, Anthropic, Google crawler activity)

This won't tell you every time an assistant used your docs internally, but it will tell you when AI systems send traffic back via citations or links.

For a B2B SaaS company

A practical stack is:

• Profound if you're a larger enterprise with a significant budget.
• Scrunch AI if your business depends heavily on technical or developer documentation.
• Goodie if your focus is monitoring visibility across multiple AI assistants.
• Otterly.AI if you're looking for a more affordable way to start measuring AI search presence.

If you tell me:

• roughly how many documentation pages you have,
• whether you're selling to developers or enterprise buyers,
• and whether your docs are public (e.g. Docusaurus, Read the Docs, GitBook, Mintlify),

I can recommend the tool that's likely to provide the best value for your specific setup.

If your goal is specifically "How often do AI assistants (ChatGPT, Claude, Gemini, Perplexity, etc.) use our documentation as a source?", you're looking for a relatively new category of products often called LLM visibility, AI search analytics, or Generative Engine Optimization (GEO).

The market is evolving quickly, but here are the main options:

For a B2B company with technical documentation, I'd divide the problem into three layers:

1. AI visibility monitoring
   • Which AI assistants mention your company?
   • Which competitors appear instead?
   • What sources are cited?
   • Which prompts trigger your docs?

2. Documentation analytics
   • Which documentation pages are being crawled by AI bots?
   • Are your docs indexed?
   • Which pages are referenced most often?

3. Traffic attribution
   • How much traffic arrives from ChatGPT, Claude, Gemini, Perplexity, etc.?
   • Which docs lead to product signups?

One important limitation

No tool can see every ChatGPT or Claude response.

Most assistants do not expose complete analytics showing:

• "Your docs were cited 8,243 times this month."

That data isn't available publicly.

Instead, these tools typically:

• run thousands of representative prompts
• check whether your brand appears
• record citations
• monitor changes over time
• compare against competitors

So they're measuring visibility, not every real user conversation.

If your docs are public

If you publish docs like:

• docs.company.com
• developer.company.com
• API reference
• knowledge base

I'd recommend combining:

• an AI visibility platform (Profound or Scrunch AI)
• your web analytics (GA4 or similar)
• server logs to measure AI crawler activity
• Search Console/Bing Webmaster for indexing

Together, those provide a much more complete picture than any single tool.

If you're a developer tools or enterprise SaaS company (for example, API documentation or SDKs), I can also recommend the tools that engineering organizations are using specifically to optimize documentation for LLM citation rather than traditional SEO.