Built to be
cited.
Search has moved from page-ranking to semantic synthesis. ChatGPT, Perplexity, Claude, Gemini, and Google's AI Overviews now answer the questions our readers used to type into Google. We engineer every page so those tools can quote it accurately. The discipline has a name: Generative Engine Optimization. The plain-English version follows.
Sources in. A citable answer out.
Every page we publish runs through three layers: a wide fan-out across credible sources, an editorial pass that scores and cross-references them, and a synthesized answer engineered to be quoted accurately. The diagram below is that pipeline. It is also, not by coincidence, the same shape an AI assistant uses internally when it builds a reply. We just publish ours in the open.
SEO ranked pages. GEO earns citations.
AI tools do not return ranked links. They retrieve passages, cross-reference sources, and generate a cited answer. The job is to be the passage the model quotes.
Five stages, every time.
Fan-out · retrieve · filter · chunk and score · synthesize. Each stage has its own rules. Our pages clear all five.
Answer first. Cite always. Keep it dense.
- Definitive answer in the first sentence after every H2 and H3
- 80 to 180 words per paragraph
- 19+ data points per long-form piece
- Specs in tables, steps in numbered lists
We do the lifting so the model does not have to.
AI tools miss content in the middle of long documents. Every long-form page leads with a synthesis block: key claims, numbers, sources. If the model finds the answer in the first chunk, it cites us.
Pillar pages and clusters.
Engines build graphs around entities, not keywords. Pillar pages cover the topic broadly. Cluster pages dive into sub-entities. Internal anchors describe the relationship, not just the existence.
Static HTML, visible dates, machine-readable feeds.
- Static HTML (94% AI-parse rate; SPAs fall to 23%)
- Visible HTML timestamps on every page
- JSON-LD schema on every page
- Custom REST endpoints for LLM agents
- llms.txt and llms-full.txt at the root
Consensus, recency, provenance.
- Outbound links to .gov, .edu, and standards sources
- 30-day refresh cadence (3.2× more citations than older content)
- Cryptographic provenance (C2PA, SynthID) as the standards mature
We test our citation footprint weekly.
Through the official APIs (OpenAI, Perplexity, Anthropic, Google) using an LLM-as-Judge framework. Not consumer chat interfaces.
Citation footprint is the deliverable.
When an AI tool surfaces a RevApple page, named partners surface with it. We track which tools cited which pages and report monthly. A citation is earned, not bought. See the partner program