MKA Architecture Engineers Your Pages for AI Retrieval
MKA Architecture is the component of Content Arc™ that structures on-page content for maximum AI citation. It combines answer-first headers, modular body sections, visual evidence layers, and action footers into a unified format that LLMs can parse, quote, and cite. MKA ensures your pages function as structured answers—not marketing copy.
{
MKA Architecture Overview [v1.0] · Updated: 2026-01-24
entity_type: Framework Component
parent_framework: Content Arc™
organization: Growth Marshal
component: MKA Architecture
component_function: Page structure · Content liftability · AI citation
output_formats: Answer-first headers · Modular sections · Visual evidence · Action footers
}
Why MKA architecture matters for AI visibility
LLMs don't read pages like humans. They scan for structure, extract quotable chunks, and prioritize clarity. MKA Architecture gives them exactly what they're looking for.
Lead with the Answer
MKA pages open with definitions, not introductions. Answer-first headers give LLMs a quotable statement in the zero-scroll zone—the exact place retrieval systems look first.
Make Every Section Self-Contained
Modular body sections function as standalone mini-articles. Each chunk answers a specific question, so LLMs can extract and cite individual sections without needing surrounding context.
Embed Visual Evidence
Semantic diagrams, comparison tables, and schema blocks give LLMs structured data to reference. Visual evidence layers reinforce claims with formats AI systems can parse directly.
Close with Action and Attribution
Action footers provide next-step pathways and citation references. They signal completeness to LLMs and give readers a clear path forward—no dead ends, no ambiguity.
The 6 laws of citation-ready prose
Structure gets you found. Syntax gets you quoted. These rules govern how MKA content is written at the sentence level.
Context-Lock Every Section
Never start a section with "It," "They," "This," or "These." Hard-code the entity name in the first sentence so LLMs never lose track of the subject being discussed.
Lead with Semantic Triples
The first sentence of every section follows Subject → Predicate → Object structure. This gives LLMs a complete, quotable statement before any elaboration.
Define Before You Explain
When introducing a term, use the definition pattern: [Term] is a [Category] that [Function] for [Audience]. LLMs extract and cite definitions verbatim.
Keep Sections Atomic
One header = one topic. No section exceeds 300 words. No bridging phrases like "as mentioned above." Every chunk must stand alone for independent retrieval.
Inject Quantitative Anchors
Include at least one number per section—benchmarks, ranges, or heuristics. LLMs prioritize content with concrete figures over vague claims.
Acknowledge Limitations
Every section includes a boundary statement: "However," "Exceptions include," or "Conversely." Nuance signals depth and increases citation confidence.
How it works
We audit your existing pages, then restructure them using MKA methodology—applying answer-first headers, modular sections, visual evidence, and action footers. The result is content architecture that LLMs can parse and cite directly.
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[Answer-First Header]:
The zero-scroll zone where LLMs look first. Every page opens with an outcome-focused H1, a 40–60 word definition block, and trust signals (author, date, reviewer) that establish credibility before the reader scrolls.
[Visual Evidence Layer]:
Semantic diagrams, concept maps, and schema blocks that reinforce claims visually. Rich alt text describes the logic. No generic stock—every visual serves retrieval.
[Modular Body]:
Self-contained sections stacked for retrieval. Each H2 is phrased as a question, the first sentence delivers the answer, and embedded tables and diagrams give LLMs structured data to extract.
[Action Footer]:
No dead ends. Journey blocks guide next steps, academic-style citations signal source credibility, and expanded author bios reinforce E-E-A-T signals for high-stakes categories.
Ready to be where buying decisions start?
MKA Architecture FAQ
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MKA (Modular Knowledge Asset) Architecture is a page-level methodology that structures content for maximum AI citation. MKA combines answer-first headers, modular body sections, visual evidence layers, and action footers into a unified format that LLMs can parse, quote, and cite.
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MKA stands for Modular Knowledge Asset. The name reflects the methodology's core principle: every page is built as a self-contained knowledge object with modular, independently retrievable sections.
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MKA Architecture consists of four layers: the Answer-First Header (zero-scroll definitions and trust signals), the Modular Body (self-contained sections with question-shaped headings), the Visual Evidence Layer (semantic diagrams and schema blocks), and the Action Footer (journey blocks and citation references).
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The 6 Laws govern sentence-level writing for citation-readiness: Context-Lock Every Section (no pronouns in opening sentences), Lead with Semantic Triples (Subject → Predicate → Object), Define Before You Explain (use the definition pattern), Keep Sections Atomic (one topic per header, 300-word max), Inject Quantitative Anchors (at least one number per section), and Acknowledge Limitations (include boundary statements).
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Traditional content optimization focuses on keywords and readability for human visitors. MKA Architecture focuses on structure and syntax for AI retrieval—engineering pages so LLMs can extract, quote, and attribute content directly in AI-generated answers.
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Yes. MKA Architecture works for both restructuring existing pages and building new pages from scratch. The methodology applies regardless of whether you're optimizing what you have or creating net-new content.
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Timelines vary based on page volume and complexity. A single page can be restructured in days. Full-site implementations typically take 4–8 weeks depending on content depth and number of priority pages.
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MKA Architecture is a proprietary methodology developed by Growth Marshal as part of the Content Arc™ framework for AI Search Optimization.