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Modern websites, ranked in AI search✦Cited by ChatGPT, Perplexity & Google AI Overviews✦32% of our own traffic comes from AI search✦Modern websites, ranked in AI search✦Cited by ChatGPT, Perplexity & Google AI Overviews✦32% of our own traffic comes from AI search✦Modern websites, ranked in AI search✦Cited by ChatGPT, Perplexity & Google AI Overviews✦32% of our own traffic comes from AI search✦Modern websites, ranked in AI search✦Cited by ChatGPT, Perplexity & Google AI Overviews✦32% of our own traffic comes from AI search✦
Creative & Conversion Design

Content designed for both humans and the AI engines that cite them.

The same insights, built in two structural layers: the human layer reads well, and the AI layer underneath ships clean schema, definitional opening paragraphs, and citation-ready claim-evidence pairs.

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The challenge
Your content might be good across the service pages, the blog posts, the FAQ pages, and the case studies. But the AI engines keep citing competitors instead of you, whether that is ChatGPT or Perplexity or Claude, and the issue is structure, not the writing. AI engines look for clear entity definitions, claims paired with evidence, short authoritative statements they can lift, and the links between concepts. Most content was written for human readers, with narrative flow and persuasive build, so it reads well but is hard for the retrieval layer to use. The competitors getting cited worked out that designing content for AI is a parallel job, the same insights arranged a different way.
How we solve it
On Story, Engine, and Studio builds, every page we ship is designed in two layers. The human layer is real content, with a narrative that holds attention, a clear visual hierarchy, and a flow that persuades. The AI layer adds structured signals (FAQPage and HowTo schema generated at build time, definitional first paragraphs, claim-evidence pairs the retrieval engines can pull verbatim, BreadcrumbList for context, semantic HTML rather than div soup). On the Local, Growth, and Authority retainers we apply the same dual-layer approach to monthly content. We train your team on the patterns and provide templates so AI-native structure becomes standard, not extra. The work shows up in your AI-traffic data on cite-met.com within 30 to 60 days.

Key benefits

Dual-Layer Content Architecture

Content built to hold a human reader and earn an AI citation at the same time: a persuasive narrative on top, with structured data and citable statements underneath for the answer engines. One asset that performs on two fronts.

Citation Pattern Matching

Content structures designed to match the patterns AI systems use when they pick a citation. We study the way ChatGPT and Perplexity, along with Gemini, phrase answers in your category, then build content that fits.

Scalable Template Systems

AEO content templates, structured-data generators, and quality scorecards your team can run without us. We are not building a dependency, we are building a capability your content team owns and scales on its own.

Measurable Citation Growth

Every content piece is tracked for AI citation performance. Monthly reports show which content gets cited, by which platforms, for which queries, and how citation rates change as you apply AEO design principles.

Our process

  1. 01

    Citation Landscape Analysis

    We analyze how AI systems currently answer questions in your category, which competitors get cited, and what structural patterns the most-cited content shares. This reveals the specific design opportunities for your content.

  2. 02

    AEO Template & Framework Design

    We create content templates, structured-data schemas, and design guidelines built for your content types. Each template bakes in the patterns that earn citations, from definition blocks and claim-evidence pairs to FAQ structures that map the relationships between entities.

  3. 03

    Content Production & Optimization

    We produce or restructure your priority content using the AEO frameworks, retrofitting your existing high-value pages with AI-native structure and designing new pages from the ground up to work on both layers.

  4. 04

    Team Training & Capability Transfer

    Your content team learns the AEO design principles, how to use the templates, and how to score quality. We stay on as advisors while the team makes the practice its own and scales AEO production without us.

3.7x

Average increase in AI citation rate for content redesigned with AEO principles

89%

Of AEO-optimized content pages receive at least one AI platform citation within 90 days

156%

Average organic traffic increase for pages with dual-layer content architecture

< 60 days

Typical time from AEO content launch to first measurable AI citation

Ready to get started?

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Explore related solutions

Answer Engine Optimization

Answer Engine Optimization is the actual technical work that gets your business mentioned by name when ChatGPT, Perplexity, Claude, and Google AI Overviews answer the questions your customers ask.

Entity Authority Building

ChatGPT, Perplexity, and Claude build internal maps of who is an authority on what. If your business is a weak entity in those maps, no amount of on-page keyword work will get you cited.

Conversion UX Design

We design for the action that pays you, a booking or a quote request, using behavioral data and steady testing rather than design by committee, so every section on the page has to earn its place.

Industries that ship this

Dentists

Dental practices sites with Dentist schema.

Lawyers

Law firms sites with LegalService schema.

Accountants and CPAs

Accounting firms sites with AccountingService schema.

Real Estate

Real estate agents and developers sites with RealEstateAgent schema.

Custom Home Builders

Custom builders sites with HomeBuilder schema.

Custom AV Installers

AV installers sites with LocalBusiness schema.

Want to verify?

Industry sources we cite.

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Quick answers

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We rebuild small business websites for AI search and keep them ranked monthly. The audit is free, the proposal comes in below your current spend, and the work is verifiable on four live case studies.

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