Find which prompts trigger page access
Separate prompts that only create screenshots from prompts that correlate with AI systems requesting important pages.
AI Visibility Attribution
Which pages do AI systems actually request?
Prompt trackers show what AI says. SeeLLM shows which pages AI systems actually request, revisit, skip, or leave crawled but not cited.
Answer-to-Access Loop
01
Prompt
A buyer, researcher, or internal team asks an AI system about a category, vendor, problem, or comparison.
02
Answer
The model decides which brands to mention, how to classify them, and what language to use.
03
Citation
Search-backed systems may cite your site, a competitor, a review platform, Reddit, docs, or a third-party article.
04
Crawl
AI crawlers and agent-like fetches touch pages before, during, or after visibility changes.
05
Fetch
The AI system, search layer, or crawler requests your page to ground, refresh, or verify an answer.
06
Extraction
The useful question is what the system can pull from the page: category, facts, evidence, comparisons, pricing, or proof.
07
Reuse
Attribution becomes useful when fetch evidence is compared with mentions, citations, and changes in the answer surface.
Definition
AI visibility attribution connects the answer to the fetch.
A screenshot of ChatGPT mentioning your brand proves visibility. It does not prove impact. AI visibility attribution connects what AI systems say with what happens around the answer: page fetches, citations, repeat crawler attention, skipped pages, and crawled-but-not-cited gaps.
The blind spot
Prompt tracking alone creates false confidence.
A brand can appear in an AI answer while its strongest pages are never requested. Another brand can appear rarely but show repeated AI fetches on pricing, docs, comparison, or signup pages.
Tool
What it shows
What it misses
Prompt tracker
Whether an AI answer mentioned your brand for a tested prompt.
Whether the model or crawler actually requested your pages before or after the answer.
AI SEO tool
Citations, answer presence, category coverage, and sometimes competitor visibility.
Which URLs AI systems fetched, skipped, revisited, or left crawled but not cited.
Web analytics
Sessions, events, funnels, and conversion behavior after a visitor arrives.
Server-side AI fetches that never become a browser session or click-through.
SeeLLM
AI answers plus backend page activity, crawler signals, important-page states, and crawl-to-citation gaps.
Perfect certainty about why a platform fetched a page. Ambiguous signals are labeled instead of overclaimed.
Backend evidence
SeeLLM starts where normal analytics usually loses the signal.
AI crawlers, agent-like fetches, and grounding requests do not look like normal click-through traffic. SeeLLM uses backend and edge-level evidence so teams can reason from the request, not only from browser JavaScript.
AI crawler hits by page
User agents, headers, and request patterns
Important pages requested by AI systems
Pricing, docs, signup, and comparison pages fetched
Pages that are crawled but not cited
Ambiguous AI activity that should be estimated, not overstated
Use cases
Move from AI share of voice to AI page access evidence.
Find brand misclassification
See when AI systems mention you in the wrong category, compare you with the wrong competitors, or reuse weak positioning.
Prioritize pages worth fixing
Focus on pages with evidence of AI attention, not only pages that already rank or receive human search traffic.
Close the crawled-but-not-cited gap
See which pages AI systems can access but do not reuse, then improve structure, evidence, and category clarity.
FAQ
Direct answers for AI visibility attribution.
What is AI visibility attribution?
AI visibility attribution is the process of connecting AI answers to backend evidence of page access. It tracks whether AI systems request, crawl, revisit, cite, skip, or leave important pages crawled but not cited.
Can AI-driven page access be tracked perfectly?
No. AI activity is incomplete because platforms can mask sources, rotate user agents, strip referrers, and fetch pages through search or crawler layers. A trustworthy system separates confirmed, likely, and ambiguous AI signals.
How is AI visibility attribution different from AI share of voice?
AI share of voice measures how often your brand appears in AI answers. AI visibility attribution asks which pages AI systems actually touched, whether important pages were reused or ignored, and where crawl evidence does not match answer visibility.
How is this different from Microsoft Clarity?
Microsoft Clarity can show AI bot activity from supported integrations. SeeLLM focuses on important-page attribution: which pages AI systems touched, where citation or referral evidence is missing, and which page-level fixes should be tracked next.
Which AI platforms can be monitored?
SeeLLM is built for AI-mediated discovery across platforms such as ChatGPT, Perplexity, Gemini, Claude, Copilot, and AI crawler traffic. Some fetch signals are direct, while others must be inferred from backend patterns and labeled carefully.
Start with proof
Measure which pages AI systems request.
Run the free scan first. Then monitor the pages that shape AI answers so your team can see which systems request, revisit, skip, or ignore them.