“We publish constantly. AI cites our competitors’ pages.”
Volume isn’t the lever — extractability is. We show which pages models actually cite and what those pages have that yours don’t.
Make pages AI wants to cite
Models don’t read your site the way buyers do — they extract, compare, and quote. Peakmark shows what AI already cites in your category and helps you build content that earns the citation.
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Written for Google, invisible to AI
“Our best comparison page never appears in AI answers.”
Models favor direct answers, clear structure, and named facts. Peakmark flags what to restructure — headings, FAQ blocks, schema — so answers can lift from your page.
“Reddit threads outrank our documentation.”
Models trust corroborated sources. See which communities and review sites feed your category’s answers, and earn presence there alongside your own pages.
Citation intelligence, then execution
Know what gets cited, then build content engineered for it.
Source intelligence
The exact domains models read and cite in your category — review sites, communities, listicles, docs — ranked by influence on your answers.
Answer-ready briefs and drafts
Content generated from prompts you’re losing: question-shaped headings, direct answers up top, facts a model can quote without guessing.
Schema and technical checks
Structured data, llms.txt, AI crawler access, rendering issues — the technical layer that decides if your content is even in the running.
Citation tracking
Watch your pages start appearing as sources in AI answers — the clearest signal your content is working.
40%
visibility lift measured for well-structured, citation-ready content in generative engine responses
Aggarwal et al., GEO research, 2023
How content optimization works
Map the sources
See which domains and pages power your category’s AI answers today — and where you’re absent.
Build to be quoted
Generate briefs and drafts targeting lost prompts, structured for extraction: direct answers, clear facts, schema in place.
Track the citations
Publish, then watch citation tracking confirm when models start reading — and quoting — your pages.
Frequently asked questions
- What is content optimization for AI answers?
- Often called GEO (generative engine optimization) content: writing and structuring pages so AI models can extract, trust, and cite them. It emphasizes direct answers, question-shaped structure, named facts and figures, schema markup, and presence on the third-party sources models already trust.
- Which sources do AI models actually cite?
- It varies by category — which is the point: it’s measurable. Typically a mix of review platforms (G2, Capterra), communities (Reddit), comparison articles, news, and vendor docs. Peakmark shows the actual list for your category and how much each source shapes your answers.
- Does classic SEO content work for AI answers?
- Partially. Crawlability and authority still matter, and search indexes feed AI retrieval. But content optimized to rank often buries the answer — long intros, thin structure. AI-ready content puts the answer first, states facts explicitly, and structures pages so models can quote them.
- Do I have to rewrite everything?
- No. Start with pages targeting your highest-value lost prompts — often a handful of comparison and category pages. Peakmark’s briefs tell you which pages and what to change, and citation tracking confirms which changes paid off.
Explore the rest of the platform
Monitoring, evidence, and execution — every piece feeds the next.
Build the pages AI quotes next
Map your category’s sources today and get your first answer-ready briefs in minutes.
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