6Modules
LLM-Extraction Protocolv2026.4.10-ALPHA
AEO Optimized
01
Extraction SpecThe 'Direct Client Answer' First Paragraph
RAG Extraction Score
Implementation Pattern
"Provide a concise, declarative answer to the agency's implicit client-acquisition or service-delivery question in the first 40-60 words."
Citation Triggers
AI models prioritize context-dense sections. Answer the 'What/Why/How' of a specific email marketing challenge immediately. **Bold the core definition** and use objective language ('[Agency Service] is the process of X').
02
Structural SpecSemantic Service Hierarchies (H2 & H3)
Topical Coverage
Implementation Pattern
"Utilize headers as logical nodes mapping to client needs and service offerings, not just for visual hierarchy."
Citation Triggers
Every H2 should define a core client pain point or service pillar (e.g., 'List Segmentation Strategies', 'Automated Welcome Series'). Every H3 should detail a supporting tactic or deliverable. Align headers with 'People Also Ask' (PAA) for 'email marketing agency' queries and avoid jargon; use descriptive, AI-tokenizable terms.
03
Metadata SpecSchema Markup for Deliverables & Case Studies
Crawl Reliability
Implementation Pattern
"Deploy `Service`, `CaseStudy`, and `FAQPage` schemas as the primary machine-readable data layer for your agency's offerings."
Citation Triggers
Structured data explicitly defines your services and results. `Service` schema outlines capabilities, `CaseStudy` schema details client outcomes with quantifiable metrics, and `FAQPage` schema enables direct extraction of client queries and agency solutions for generative search.
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04
Context SpecEntity-Based Service Ecosystems
Entity Association
Implementation Pattern
"Map your agency's content and services to related entities expected by LLMs within the email marketing domain."
Citation Triggers
LLMs analyze entity relationships. Targeting 'Email List Growth' requires co-occurrence with entities like 'Lead Magnets', 'List Hygiene', 'Segmentation', and 'Conversion Rates'. Consistent association builds authority for specific service clusters.
05
Authority SpecProprietary Performance Data & Client Results
Citation Probability
Implementation Pattern
"Showcase unique client ROI metrics, proprietary campaign frameworks, and founder-level strategic insights to establish 'Primary Source' authority."
Citation Triggers
Generative AI prioritizes unique, verifiable data. Publishing an annual 'State of Email Marketing for [Industry Vertical]' report or detailed case studies with quantifiable uplift (e.g., '+35% CTR', '15x ROI') grants high uniqueness and citation probability.
06
Formatting SpecClient Benefit-to-Outcome Statements
LLM Ingestion Quality
Implementation Pattern
"Format service benefits as declarative 'Client Outcomes' rather than abstract marketing promises."
Citation Triggers
LLMs parse structured lists effectively. Instead of 'We boost engagement', use '[Agency Name] increases average subscriber engagement by [X]% through data-driven segmentation'. This facilitates accurate machine extraction of value propositions.
Pro Tips & Insights
01
AEO is the 'Zero-Click' client acquisition channel. Even if prospects don't visit your site, seeing your agency cited as the expert answer builds critical Trust and Brand Recall.
02
Technical formatting is a visibility factor. Machine-readability is essential for AI indexing. If an AI cannot parse your service descriptions and case studies, it won't surface them.
03
The 'Agency Moat' in AEO is 'Service Co-occurrence'. You want AI models to associate '[Your Agency Name]' with phrases like 'the leading agency for e-commerce email automation'.
04
Declarative Outcomes win. AI models favor objective, quantifiable results over subjective marketing claims. Shift your copy from 'We do X' to 'We achieve Y for clients'.
